scholarly journals An Integrated District Mapping Strategy for Loiasis to Enable Safe Mass Treatment for Onchocerciasis in Gabon

2021 ◽  
Author(s):  
Sylvie Ntsame Ella ◽  
Kisito Ogoussan ◽  
Katherine Gass ◽  
Lee Hundley ◽  
Peter J. Diggle ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Rapid Test ◽  
Treatment Decisions ◽  
Ivermectin Treatment ◽  
Mass Treatment ◽  
Modeling Tools ◽  
Prediction Tools ◽  
Mapping Strategy ◽  
Mapping Approach ◽  
Endemic Areas

The lack of a WHO-recommended strategy for onchocerciasis treatment with ivermectin in hypo-endemic areas co-endemic with loiasis is an impediment to global onchocerciasis elimination. New loiasis diagnostics (LoaScope; Loa antibody rapid test) and risk prediction tools may enable safe mass treatment decisions in co-endemic areas. In 2017–2018, an integrated mapping strategy for onchocerciasis, lymphatic filariasis (LF), and loiasis, aimed at enabling safe ivermectin treatment decisions, was piloted in Gabon. Three ivermectin-naïve departments suspected to be hypo-endemic were selected and up to 100 adults per village across 30 villages in each of the three departments underwent testing for indicators of onchocerciasis, LF, and loiasis. An additional 67 communities in five adjoining departments were tested for loiasis to extend the prevalence and intensity predictions and possibly expand the boundaries of areas deemed safe for ivermectin treatment. Integrated testing in the three departments revealed within-department heterogeneity for all the three diseases, highlighting the value of a mapping approach that relies on cluster-based sampling rather than sentinel sites. These results suggest that safe mass treatment of onchocerciasis may be possible at the subdepartment level, even in departments where loiasis is present. Beyond valuable epidemiologic data, the study generated insight into the performance of various diagnostics and the feasibility of an integrated mapping approach utilizing new diagnostic and modeling tools. Further research should explore how programs can combine these diagnostic and risk prediction tools into a feasible programmatic strategy to enable safe treatment decisions where loiasis and onchocerciasis are co-endemic.

scholarly journals Test characteristics of history, examination and investigations in the evaluation for septic arthritis in the child presenting with acute non-traumatic limp. A systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038088
Author(s):  
Jacky Tu ◽  
Peter Gowdie ◽  
Julian Cassar ◽  
Simon Craig
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Quality Assessment ◽  
Likelihood Ratio ◽  
Risk Prediction ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Prediction Tools ◽  
Clinical Risk

BackgroundSeptic arthritis is an uncommon but potentially significant diagnosis to be considered when a child presents to the emergency department (ED) with non-traumatic limp. Our objective was to determine the diagnostic accuracy of clinical findings (history and examination) and investigation results (pathology tests and imaging) for the diagnosis of septic arthritis among children presenting with acute non-traumatic limp to the ED.MethodsSystematic review of the literature published between 1966 and June 2019 on MEDLINE and EMBASE databases. Studies were included if they evaluated children presenting with lower limb complaints and evaluated diagnostic performance of items from history, physical examination, laboratory testing or radiological examination. Data were independently extracted by two authors, and quality assessment was performed using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 tool.Results18 studies were identified, and included 2672 children (560 with a final diagnosis of septic arthritis). There was substantial heterogeneity in inclusion criteria, study setting, definitions of specific variables and the gold standard used to confirm septic arthritis. Clinical and investigation findings were reported using varying definitions and cut-offs, and applied to differing study populations. Spectrum bias and poor-to-moderate study design quality limit their applicability to the ED setting.Single studies suggest that the presence of joint tenderness (n=189; positive likelihood ratio 11.4 (95% CI 5.9 to 22.0); negative likelihood ratio 0.2 (95% CI 0.0 to 1.2)) and joint effusion on ultrasound (n=127; positive likelihood ratio 8.4 (95% CI 4.1 to 17.1); negative likelihood ratio 0.2 (95% CI 0.1 to 0.3)) appear to be useful. Two promising clinical risk prediction tools were identified, however, their performance was notably lower when tested in external validation studies.DiscussionDifferentiating children with septic arthritis from non-emergent disorders of non-traumatic limp remains a key diagnostic challenge for emergency physicians. There is a need for prospectively derived and validated ED-based clinical risk prediction tools.

scholarly journals P90 PERCEIVE: PrEdiction of Risk and Communication of outcome following major lower limb amputation - a collaboratIVE study

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
B Gwilym ◽  
C Waldron ◽  
E Thomas-Jones ◽  
P Pallmann ◽  
R Preece ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Lower Limb ◽  
Health Care Professionals ◽  
Healthcare Professionals ◽  
Demographic Data ◽  
Collaborative Study ◽  
Lower Limb Amputation ◽  
Research Network ◽  
Limb Amputation ◽  
Prediction Tools

Abstract Introduction Major Lower Limb Amputation (MLLA) is a life changing event with significant morbidity and mortality. Inaccurate risk prediction can lead to poor decision making, resulting in delay to definitive surgery, or undertaking amputation when not in the patient’s best interest. We aim to answer: In adult patients undergoing MLLA for chronic limb threatening ischaemia or diabetes, how accurately do health care professionals prospectively predict outcomes after MLLA, and how does this compare to existing prediction tools? Methods A multicentre prospective observational cohort study is being delivered through the Vascular and Endovascular Research Network. Dissemination was via an existing network of contacts and social media. Consecutive data will be collected for seven months from site launch date, including demographic data and pre-operative outcome predictions from surgeons, anaesthetists, and allied healthcare professionals. Follow-up data will comprise 30-day (mortality, morbidity, MLLA revision, surgical site infection, and blood transfusion) and 1-year (mortality, MLLA revision and ambulation). The accuracy of surgeons’ predictions will be evaluated and compared to pre-existing risk prediction scoring tools. Results PERCEIVE launched on 01/10/2020 with 23 centres (16 UK, 7 international) registered to collect data. 50 other centres (27 UK, 23 international) have expressed interest/are pursuing local audit/ethical approval. We aim to collect data on clinicians estimate of outcomes for over 500 patients. Discussion This study will utilise a trainee research network to provide data on the accuracy of healthcare professionals’ predictions of outcomes following MLLA and compare this to the utility of existing prediction tools in this patient cohort.

Validation of risk prediction tools in elderly patients who initiate dialysis

2019 ◽  
Vol 51 (7) ◽  
pp. 1231-1238 ◽  
Author(s):  
Dohui Hwang ◽  
Eunbin Lee ◽  
Samel Park ◽  
Byung Chul Yoo ◽  
Suyeon Park ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Risk Prediction ◽  
Prediction Tools

A review of literature on risk prediction tools for hospital readmissions in older adults

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Chronic Obstructive ◽  
Risk Category ◽  
Extensive Literature ◽  
Content Type ◽  
Prediction Tools ◽  
Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

scholarly journals Response to ‘Clinical relevance and validity of obesity risk prediction tools’ by Redsell et al.

2018 ◽  
Vol 21 (17) ◽  
pp. 3149-3150
Author(s):  
Oliver J Canfell ◽  
Robyn Littlewood ◽  
Olivia RL Wright ◽  
Jacqueline L Walker
Keyword(s):  
Risk Prediction ◽  
Clinical Relevance ◽  
Obesity Risk ◽  
Prediction Tools

scholarly journals Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Tolksdorf ◽  
Michael W. Kattan ◽  
Stephen A. Boorjian ◽  
Stephen J. Freedland ◽  
Karim Saba ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prostate Biopsy ◽  
Prostate Cancer Risk ◽  
High Grade ◽  
Prediction Tools ◽  
History Of

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

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