Bilateral cystoid macular edema as the presenting feature of chronic myeloid leukemia.

PURPOSE: Ophthalmic disease may rarely be a presenting feature of chronic myeloid leukemia (CML). METHODS: We report a case of a 53-year-old man with type 1 diabetes mellitus who presented with a rapid onset of bilateral blurred vision. RESULTS: He was noted to have bilateral macular edema and was initially treated for presumed diabetic macular edema (DME) with intravitreal alifbercept injections. One month later, there was complete resolution of his macular edema. Review of his history and imaging revealed features atypical for DME, specifically; the rapid onset of bilateral blurred vision over 2-3 weeks, numerous cotton wool spots within the macula, the absence of any exudates, the symmetrical macular edema with a "vaulted ceiling" appearance (more typical of cystoid macular edema) and the dramatic response to a single intravitreal aflibercept injection. One week after his intravitreal injection, the patient was diagnosed with CML following marked leucocytosis on a routine blood test by his general practitioner. Although uncommon, sudden onset bilateral edema in the absence of other chronic diabetic changes should prompt consideration of an underlying haematological cause. CONCLUSION: This case highlights the importance of considering CML as a differential diagnosis in patients presenting with sudden onset, bilateral cystoid macular oedema. Vigilance is especially important in patients with co-existing diabetic retinopathy as the clinical features of leukemic retinopathy can overlap. Furthermore, the diagnosis of CML in a patient with diabetes mellitus should prompt extra observation for accelerated worsening of diabetic retinopathy.

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups. OBJECTIVE: To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms. DESIGN, SETTING, AND PARTICIPANTS: Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined. RESULTS AND LIMITATIONS: There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups. CONCLUSIONS: The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer. PATIENT SUMMARY: We previously developed a calculator that predicts patients' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.

Severe mental illness: cardiovascular risk assessment and management.

Patients with severe mental illness (SMI) including schizophrenia and bipolar disorder die on average 15-20 years earlier than the general population often due to sudden death that, in most cases, is caused by cardiovascular disease. This state-of-the-art review aims to address the complex association between SMI and cardiovascular risk, explore disparities in cardiovascular care pathways, describe how to adequately predict cardiovascular outcomes, and propose targeted interventions to improve cardiovascular health in patients with SMI. These patients have an adverse cardiovascular risk factor profile due to an interplay between biological factors such as chronic inflammation, patient factors such as excessive smoking, and healthcare system factors such as stigma and discrimination. Several disparities in cardiovascular care pathways have been demonstrated in patients with SMI, resulting in a 47% lower likelihood of undergoing invasive coronary procedures and substantially lower rates of prescribed standard secondary prevention medications compared with the general population. Although early cardiovascular risk prediction is important, conventional risk prediction models do not accurately predict long-term cardiovascular outcomes as cardiovascular disease and mortality are only partly driven by traditional risk factors in this patient group. As such, SMI-specific risk prediction models and clinical tools such as the electrocardiogram and echocardiogram are necessary when assessing and managing cardiovascular risk associated with SMI. In conclusion, there is a necessity for differentiated cardiovascular care in patients with SMI. By addressing factors involved in the excess cardiovascular risk, reconsidering risk stratification approaches, and implementing multidisciplinary care models, clinicians can take steps towards improving cardiovascular health and long-term outcomes in patients with SMI.

Retinal Oxygenation With Conventional 100-ms Versus Short-Pulse Pan-Retinal Laser Photocoagulation.

BACKGROUND AND OBJECTIVE: Conventional (100 ms) pan-retinal photocoagulation (PRP) laser burns are larger than short-pulse (10 ms to 20 ms) PRP burns. This study investigates the effect of PRP burns of different sizes on retinal oxygenation. METHOD: A mathematical model using COMSOL Multiphysics 6 was used to create a three-dimensional abstraction of the coupled biology of the choroid, photoreceptor, and retinal tissues. Laser burn sizes were varied in the model, specifically considering burn diameters of 500 µm, 250 µm, and 125 µm, while keeping the total burn area constant. RESULTS: Total increase in retinal oxygenation was the same for different burn sizes, but the oxygen distribution differed. Smaller burns resulted in a more even lateral oxygen distribution but with reduced penetration into the inner retina. CONCLUSIONS: Conventional and short-pulse PRP may affect retinal oxygenation differently, even when total burn area is the same. Further investigation into optimum burn size and pattern is required. [Ophthalmic Surg Lasers Imaging Retina 2024;55:40-45.].