Optimized serum ferritin prediction of iron overload in transfusion-dependent thalassemia: likelihood ratio and age-adjustment approach.

BACKGROUND: Early detection of iron overload in transfusion-dependent thalassemia (TDT) patients is critical to prevent complications and improve survival. OBJECTIVES: Evaluate the utility of serum ferritin (SF) in the prediction of hepatic and myocardial iron overload (HIO and MIO) compared to T2*-MRI. DESIGN: Retrospective SETTINGS: Governmental hospitals. PATIENTS AND METHODS: Patients with TDT who had T2*-MRI examinations between January 2016 to October 2019 were included. The predictive value of SF for detection of HIO and MIO was assessed by measuring area under the curve (AUC). A sample size of 123 cases was calculated to detect a correlation of 0.25 with 90% power and a two-sided type I error of 0.05. MAIN OUTCOME MEASURES: The correlation between SF and estimated hepatic iron concentration. SAMPLE SIZE: 137 TDT patients who required regular blood transfusions. RESULTS: The predictive value of SF was excellent for detection of HIO (AUC=0.83-0.87) but fair for detection of MIO (AUC=0.67). The two independent predictors of MIO were age and SF. The log of (age × SF) enhanced the SF predictive value for MIO (AUC=0.78). SF values of 700 and 1250 mg/L effectively excluded mild and moderate HIO with a sensitivity of 97.8% and 94.2%, respectively (LR-=0.1). While SF values of 1640 and 2150 mg/L accurately diagnosed mild and moderate HIO with a specificity of 95.55% and 96.4%, respectively (LR+>10). A log of (age × SF) cut-off value of 4.15 effectively excluded MIO (LR-=0.1), while a value of 4.65 moderately confirmed MIO (LR+=3.2). CONCLUSIONS: SF is an excellent predictor of hepatic IO in TDT. Age adjustment enhanced its myocardial IO predictive accuracy. Likelihood ratio-based SF cut-off values may help clinicians in risk stratification and treatment decision-making. LIMITATIONS: The laboratory data were gathered retrospectively and although the risk of selection bias for T2*-MRI examination is thought to be low, it cannot be ignored. CONFLICT OF INTEREST: None.

Predicting Severe COVID-19 Infection on Initial Diagnosis: Comparison and Validation of CT Imaging Triage Tools.

BACKGROUND: Developing a reliable predictive tool of disease severity in COVID-19 infection is important to help triage patients and ensure the appropriate utilization of health-care resources. OBJECTIVE: To develop, validate, and compare three CT scoring systems (CTSS) to predict severe disease on initial diagnosis of COVID-19 infection. METHODS: One hundred and twenty and 80 symptomatic adults with confirmed COVID-19 infection who presented to emergency department were evaluated retrospectively in the primary and validation groups, respectively. All patients had non-contrast CT chest within 48 hours of admission. Three lobarbased CTSS were assessed and compared. The simple lobar system was based on the extent of pulmonary infiltration. Attenuation corrected lobar system (ACL) assigned further weighting factor based on attenuation of pulmonary infiltrates. Attenuation and volume-corrected lobar system incorporated further weighting factor based on proportional lobar volume. The total CT severity score (TSS) was calculated by adding individual lobar scores. The disease severity assessment was based on Chinese National Health Commission guidelines. Disease severity discrimination was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: The ACL CTSS demonstrated the best predictive and consistent accuracy of disease severity with an AUC of 0.93(95%CI:0.88-0.97) in the primary cohort and 0.97 (95%CI:0.91.5-1) in the validation group. Applying a TSS cut-off value of 9.25, the sensitivities were 96.4% and 100% and the specificities were 75% and 91% in the primary and validation groups, respectively. CONCLUSION: The ACL CTSS showed the highest accuracy and consistency in predicting severe disease on initial diagnosis of COVID-19. This scoring system may provide frontline physicians with a triage tool to guide admission, discharge, and early detection of severe illness.

Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools.

RATIONALE: This study was conducted to develop, validate, and compare prediction models for severe disease and critical illness among symptomatic patients with confirmed COVID-19. METHODS: For development cohort, 433 symptomatic patients diagnosed with COVID-19 between April 15th 2020 and June 30th, 2020 presented to Tawam Public Hospital, Abu Dhabi, United Arab Emirates were included in this study. Our cohort included both severe and non-severe patients as all cases were admitted for purpose of isolation as per hospital policy. We examined 19 potential predictors of severe disease and critical illness that were recorded at the time of initial assessment. Univariate and multivariate logistic regression analyses were used to construct predictive models. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration and goodness of fit of the models were assessed. A cohort of 213 patients assessed at another public hospital in the country during the same period was used to validate the models. RESULTS: One hundred and eighty-six patients were classified as severe while the remaining 247 were categorized as non-severe. For prediction of progression to severe disease, the three independent predictive factors were age, serum lactate dehydrogenase (LDH) and serum albumin (ALA model). For progression to critical illness, the four independent predictive factors were age, serum LDH, kidney function (eGFR), and serum albumin (ALKA model). The AUC for the ALA and ALKA models were 0.88 (95% CI, 0.86-0.89) and 0.85 (95% CI, 0.83-0.86), respectively. Calibration of the two models showed good fit and the validation cohort showed excellent discrimination, with an AUC of 0.91 (95% CI, 0.83-0.99) for the ALA model and 0.89 (95% CI, 0.80-0.99) for the ALKA model. A free web-based risk calculator was developed. CONCLUSIONS: The ALA and ALKA predictive models were developed and validated based on simple, readily available clinical and laboratory tests assessed at presentation. These models may help frontline clinicians to triage patients for admission or discharge, as well as for early identification of patients at risk of developing critical illness.

Uterine Artery Embolization of Uterine Leiomyomas: Predictive MRI Features of Volumetric Response.

OBJECTIVE. The purpose of this article was to evaluate MRI features of uterine leiomyomas that predict volumetric response after uterine artery embolization (UAE). MATERIALS AND METHODS. This retrospective study included 75 patients with 212 uterine leiomyomas who were successfully treated between August 2013 and December 2018. To predict uterine volumetric response, age, number of lesions, and baseline uterine volume were assessed. To predict leiomyoma volumetric response, a multivariate regression analysis was performed to evaluate six predictive factors: location, baseline leiomyoma volume, signal intensity on T1-weighted and T2-weighted MRI, heterogeneity of signal intensity on T2-weighted MRI, and vascularity on subtraction imaging (SI). A five-variable predictive ROC model was developed to evaluate the diagnostic accuracy of the signal intensity ratio on T2-weighted MRI, enhancement ratio, heterogeneity ratio on T2-weighted MRI, location, and baseline leiomyoma volume in predicting at least 40% leiomyoma volumetric response. RESULTS. Age, number of leiomyomas, and baseline uterine volume were not predictive of uterine volumetric response. A submucosal location was the best predictive factor of leiomyoma volumetric response, and it showed 32.2% more leiomyoma volumetric response compared with a nonsubmucosal location (p < .001). Hyperintensity on T2-weighted MRI was the second best predictive factor of leiomyoma volumetric response, and it showed 16.9% more volumetric response compared with hypointense leiomyomas (p = .013). A small baseline leiomyoma volume (< 58 cm3) was associated with 10.2% more leiomyoma volumetric response compared with larger leiomyomas (p = .01). Leiomyomas that were hyperintense on SI showed 7.9% more leiomyoma volumetric response compared with those that were hypointense (p = .014). The five-variable ROC model showed high diagnostic accuracy with an AUC of 0.85, sensitivity of 82%, and specificity of 71%. CONCLUSION. A submucosal location, hyperintensity on T2-weighted MRI, small baseline leiomyoma volume (< 58 cm3), and hyperintense leiomyoma on subtraction imaging are the main independent favorable predictors of leiomyoma volumetric response after UAE. An accurate predictive ROC model was developed that may help in selecting patients suitable for UAE. Quantitative assessment of heterogeneity on T2-weighted MRI showed promising results as a predictor of volumetric response, and further research in this area using texture analysis and radiomics is suggested.

Pathological nature of renal tumors - does size matter?

INTRODUCTION: We examined the relationship between the size and nature of renal masses in term of malignant potential, histological grading, pathological staging and presence of necrosis and sarcomatoid changes. MATERIALS AND METHODS: Retrospectively, we reviewed 323 consecutive nephrectomies between 2000 and 2010. Final pathology was correlated with tumour size. The renal tumours were stratified into three groups according to the largest diameter, defined as 4 cm or smaller, greater than 4 cm to 7 cm, and greater than 7 cm. We recorded the proportion of benign tumours, tumour grade and stage, presence of necrosis and sarcomatoid change. RESULTS: Small renal masses ≤4 cm (SRMs) were more likely to be localised to the kidney (90%) and of lower histological grade (75%). The proportion of benign tumours in SRMs (15%) was higher than other two groups with the majority of benign tumours being oncocytomas. There was a statistically significant trend with greater necrosis and sarcomatoid change for the large size group. CONCLUSIONS: SRMs are likely to be low grade and organ confined with little or no adverse pathological features. There is increased likelihood of benignity in SRTs with the majority of benign tumours being oncocytomas.