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Screening for Chikungunya virus infection in aged people: Development and internal validation of a new score.

ABSTRACT: This study aimed to derive and validate a score for Chikungunya virus (CHIKV) infection screening in old people admitted to acute care units.This study was performed in the Martinique University Hospitals from retrospective cases. Patients were aged 65+, admitted to acute care units for suspected CHIKV infection in 2014, with biological testing using Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR was used as the gold standard. A screening score was created using adjusted odds ratios of factors associated with positive RT-PCR derived from a multivariable logistic regression model. A ROC curve was used to determine the best cut-off of the score. Bootstrap analysis was used to evaluate its internal validity.In all, 687 patients were included, 68% with confirmed CHIKV infection, and 32% with laboratory-unconfirmed CHIKV infection. Mean age was 80±8 years, 51% were women. Four variables were found to be independently associated with positive RT-PCR (fever: 3 points; arthralgia of the ankle: 2 points; lymphopenia: 6 points; absence of neutrophil leucocytosis: 10 points). The best cut-off was score ≥12; sensitivity was 87% (83%-90%) and specificity was 70% (63%-76%).This score shows good diagnostic performance and good internal validation and could be helpful to screen aged people for CHIKV infection.

SUBMITTER: Godaert L

PROVIDER: S-EPMC5560672 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:Background: Mortality from multidrug-resistant (MDR) pathogens is an urgent healthcare crisis worldwide. At present we do not have any simplified screening tools to predict the risk of mortality associated with MDR infections. The aim of this study was to develop a screening tool to predict mortality in patients with multidrug-resistant organisms. Methods: A retrospective cohort study to evaluate mortality risks in patients with MDR infections was conducted at Phrae Hospital. Univariable and multivariable analyses were used to classify possible risk factors. The model performance was internally validated utilizing the mean of three measures of discrimination corrected by the optimism using a 1000-bootstrap procedure. The coefficients were transformed into item scores by dividing each coefficient with the lowest coefficient and then rounding to the most adjacent number. The area under the receiver operating characteristic curve (AuROC) was used to determine the performance of the model. Results: Between 1 October 2018 and 30 September 2020, a total of 504 patients with MDR infections were enrolled. The ICU-SEPSA score composed of eight clinical risk factors: 1) immunocompromised host, 2) chronic obstructive pulmonary disease, 3) urinary tract infection, 4) sepsis, 5) placement of endotracheal tube, 6) pneumonia, 7) septic shock, and 8) use of antibiotics within the past 3 months. The model showed good calibration (Hosmer-Lemeshow χ2 = 19.27; p-value = 0.50) and good discrimination after optimism correction (AuROC 84.6%, 95% confidence interval [Cl]: 81.0%–88.0%). The positive likelihood ratio of low risk (score ≤ 5) and high risk (score ≥ 8) were 2.07 (95% CI: 1.74–2.46) and 12.35 (95% CI: 4.90–31.13), respectively. Conclusion: A simplified predictive scoring tool wad developed to predict mortality in patients with MDR infections. Due to a single-study design of this study, external validation of the results before applying in other clinical practice settings is warranted.

Project description:ObjectiveAsymptomatic carotid stenosis (ACS) is associated with an increased risk of ischaemic stroke and myocardial infarction. Risk scores have been developed to detect individuals at high risk of ACS, thereby enabling targeted screening, but previous external validation showed scope for refinement of prediction by adding additional predictors. The aim of this study was to develop a novel risk score in a large contemporary screened population.MethodsA prediction model was developed for moderate (≥50%) and severe (≥70%) ACS using data from 596 469 individuals who attended screening clinics. Variables that predicted the presence of ≥50% and ≥70% ACS independently were determined using multivariable logistic regression. Internal validation was performed using bootstrapping techniques. Discrimination was assessed using area under the receiver operating characteristic curves (AUROCs) and agreement between predicted and observed cases using calibration plots.ResultsPredictors of ≥50% and ≥70% ACS were age, sex, current smoking, diabetes mellitus, prior stroke/transient ischaemic attack, coronary artery disease, peripheral arterial disease, blood pressure, and blood lipids. Models discriminated between participants with and without ACS reliably, with an AUROC of 0.78 (95% confidence interval [CI] 0.77-0.78) for ≥ 50% ACS and 0.82 (95% CI 0.81-0.82) for ≥ 70% ACS. The number needed to screen in the highest decile of predicted risk to detect one case with ≥50% ACS was 13 and that of ≥70% ACS was 58. Targeted screening of the highest decile identified 41% of cases with ≥50% ACS and 51% with ≥70% ACS.ConclusionThe novel risk model predicted the prevalence of ACS reliably and performed better than previous models. Targeted screening among the highest decile of predicted risk identified around 40% of all cases with ≥50% ACS. Initiation or intensification of cardiovascular risk management in detected cases might help to reduce both carotid related ischaemic strokes and myocardial infarctions.

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Screening for Chikungunya virus infection in aged people: Development and internal validation of a new score.

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