Diagnostic Research: theory and application

To set a diagnosis in a patient is one of the key challenges in medical practice and forms the basis for clinical care. Diagnosis is not an aim in itself but is relevant in as far as it directs treatment and indicates the prognosis of the patient. Diagnosis amounts to an estimation of the probability of the presence of a particular disease in view of all diagnostic information (patient history, physical examination and test results) in order to decide whether treatment should be initiated or not. A diagnosis is rarely based on one single variable or test and therefore is a multivariable concern per se. However, most diagnostic studies or studies in which diagnostic tests are evaluated still follow a univariable approach. This means that a diagnostic test is evaluated in isolation without explicit regard to the clinical context in which the test is applied. In this respect, clinical practice and diagnostic research frequently do not cohere. In applied medical research of the last decades, little attention has been paid to the principles of diagnostic studies compared to, for example, etiologic studies and studies of treatment efficacy

Intensive care performance: how should we monitor performance in the future?

Abstract Intensive care faces economic challenges. Therefore evidence proving both effectiveness and efficiency, i.e. cost-effectiveness, of delivered care is needed. Today, the quality of care is an important issue in the health care debate. How do we measure quality of care, and how accurate and representative is this measurement? In the fol

Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals

Background\ud The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.\ud \ud Methods\ud In a large Dutch population cohort (n = 21,992) we classified individuals to low (<5%) and high (≥5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.\ud \ud Results\ud Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.\ud \ud Discussion\ud In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added biomarkers or imaging tests are costly or burdensome such a tailored implementation strategy may save resources and improve (cost-)effectivenes

The Market

2016 essay contest honorable mention Clare Belott\u27s The Marke

Prognosis and prognostic factors of patients with mesothelioma:A population-based study

BACKGROUND: It is important to regularly update survival estimates of patients with malignant mesothelioma as prognosis may vary according to epidemiologic factors and diagnostic and therapeutic management. METHODS: We assessed overall (baseline) survival as well as related prognostic variables in a large cohort of 1353 patients with a confirmed diagnosis of malignant mesothelioma between 2005 and 2008. RESULTS: About 50% of the patients were 70 years or older at diagnosis and the median latency time since start of asbestos exposure was 49 years. One year after diagnosis, 47% of the patients were alive, 20% after 2 years and 15% after 3 years. Prognostic variables independently associated with worse survival were: older age (HR=1.04 per year 95% CI (1.03–1.06)), sarcomatoid subtype (HR=2.45 95% CI (2.06–2.90)) and non-pleural localisation (HR=1.67 95% CI (1.26–2.22)). CONCLUSION: Survival of patients with malignant mesothelioma is still limited and depends highly on patient age, mesothelioma subtype and localisation. In addition, a substantial part of the patients had a long latency time between asbestos exposure and diagnosis

Incorporating published univariable associations in diagnostic and prognostic modeling

Background: Diagnostic and prognostic literature is overwhelmed with studies reporting univariable predictor-outcome associations. Currently, methods to incorporate such information in the construction of a prediction model are underdeveloped and unfamiliar to many researchers. Methods. This article aims to improve upon an adaptation method originally proposed by Greenland (1987) and Steyerberg (2000) to incorporate previously published univariable associations in the construction of a novel prediction model. The proposed method improves upon the variance estimation component by reconfiguring the adaptation process in established theory and making it more robust. Different variants of the proposed method were tested in a simulation study, where performance was measured by comparing estimated associations with their predefined values according to the Mean Squared Error and coverage of the 90% confidence intervals. Results: Results demonstrate that performance of estimated multivariable associations considerably improves for small datasets where external evidence is included. Although the error of estimated associations decreases with increasing amount of individual participant data, it does not disappear completely, even in very large datasets. Conclusions: The proposed method to aggregate previously published univariable associations with individual participant data in the construction of a novel prediction models outperforms established approaches and is especially worthwhile when relatively limited individual participant data are available

Hospital Standardized Mortality Ratio: Consequences of Adjusting Hospital Mortality with Indirect Standardization

Background: The hospital standardized mortality ratio (HSMR) is developed to evaluate and improve hospital quality. Different methods can be used to standardize the hospital mortality ratio. Our aim was to assess the validity and applicability of directly and indirectly standardized hospital mortality ratios. Methods: Retrospective scenario analysis using routinely collected hospital data to compare deaths predicted by the indirectly standardized case-mix adjustment method with observed deaths. Discharges from Dutch hospitals in the period 2003-2009 were used to estimate the underlying prediction models. We analysed variation in indirectly standardized hospital mortality ratios (HSMRs) when changing the case-mix distributions using different scenarios. Sixty-one Dutch hospitals were included in our scenario analysis. Results: A numerical example showed that when interaction between hospital and case-mix is present and case-mix differs between hospitals, indirectly standardized HSMRs vary between hospitals providing the same quality of care. In empirical data analysis, the differences between directly and indirectly standardized HSMRs for individual hospitals were limited. Conclusion: Direct standardization is not affected by the presence of interaction between hospital and case-mix and is therefore theoretically preferable over indirect standardization. Since direct standardization is practically impossible when multiple predictors are included in the case-mix adjustment model, indirect standardization is the only available method to compute the HSMR. Before interpreting such indirectly standardized HSMRs the case-mix distributions of individual hospitals and the presence of interactions between hospital and case-mix should be assessed