Risk prediction models in perioperative medicine: methodological considerations

Identifying individuals who are at increased risk of mortality or major morbidity following surgical procedures is an important challenge in patient management. Multivariable risk prediction models (e.g., EuroSCORE, POSSUM, and the STS Risk Calculator) have been developed to help surgeons calculate a patient’s risk of mortality using a combination of risk factors. These prediction models have transformed preoperative risk assessment. Studies evaluating the performance of risk prediction models in this and other areas of medicine have, however, been characterized by poor design, methodological conduct, and reporting. We discuss the main methodological considerations behind risk prediction models and critically discuss issues in their design, validation, and transparency

Prevalence of chronic kidney disease in the community in the United Kingdom in OxRen, a population-based cohort study

Background: Chronic kidney disease (CKD) is a largely asymptomatic condition of diminished renal function, which may not be detected until advanced stages without screening. Aim: To establish undiagnosed and overall CKD prevalence using a cross-sectional analysis. Design and Setting: Longitudinal cohort study in UK primary care. Method: Participants aged ≥60 years were invited to attend CKD screening visits to determine whether they had reduced renal function (estimated glomerular filtration rate [eGFR] <60 ml/min/1.73 m2 or albumin:creatinine ratio ≥3 mg/mmol). Those with existing CKD, low eGFR, evidence of albuminuria, or two positive screening tests attended a baseline assessment (CKD cohort). Results: A total of 3207 participants were recruited and 861 attended the baseline assessment. The CKD cohort consisted of 327 people with existing CKD, 257 people with CKD diagnosed through screening (CKD prevalence of 18.2%, 95% confidence interval [CI] = 16.9 to 19.6), and 277 with borderline/transient decreased renal function. In the CKD cohort, 54.4% were female, mean standard deviation (SD) age was 74.0 (SD 6.9) years, and mean eGFR was 58.0 (SD 18.4) ml/min/1.73 m2. Of the 584 with confirmed CKD, 44.0% were diagnosed through screening. Over half of the CKD cohort (51.9%, 447/861) fell into CKD stages 3–5 at their baseline assessment, giving an overall prevalence of CKD stages 3–5 of 13.9% (95% CI = 12.8 to 15.1). More people had reduced eGFR using the Modification of Diet in Renal Disease (MDRD) equation than with CKD Epidemiology Collaboration (CKD-EPI) equation in the 60–75-year age group and more had reduced eGFR using CKD-EPI in the ≥80-year age group. Conclusion: This study found that around 44.0% of people living with CKD are undiagnosed without screening, and prevalence of CKD stages 1–5 was 18.2% in participants aged >60 years. Follow-up will provide data on annual incidence, rate of CKD progression, determinants of rapid progression, and predictors of cardiovascular events

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require labeled data and hence force a filtering of the available data to retain only those data that have valid labels (from the psychiatric assessment). To avoid limitations of supervised learning, in this paper we investigate the use of a semi-supervised learning approach that combines both labeled and unlabeled data to derive a model for BD episode prediction. Specifically we apply the DISSFCM (Dynamic Incremental Semi-Supervised Fuzzy C-Means) algorithm which offers the possibility to process in an incremental fashion the data stream of the voice signal captured by the smartphone, thus exploiting the evolving time structure of data which is ignored by static learning methods. DISSFCM processes data in form of chunks and creates a dynamic collection of clusters thanks to a splitting mechanism that generates new clusters to better capture the hidden geometrical structure of data. This gives DISSFCM the ability to detect changes in data and dynamically adapt the model to them, thus improving the prediction accuracy. Preliminary results on real-world data collected at the Department of Affective Disorders, Institute of Psychiatry and Neurology in Warsaw (Poland) show that DISSFCM is able to predict some of healthy episodes (euthymia) and disease episodes even when only 25% of labeled data are available. Moreover DISSFM performs better than its previous version without split (ISSFCM) and it also overcomes the batch algorithm (SSFCM) that uses the whole dataset to create the model