Development and Validation of Risk Matrices for Crohn’s Disease Outcomes in Patients Who Underwent Early Therapeutic Interventions

Dias, Cláudia Camila; Rodrigues, Pedro Pereira; Coelho, Rosa; Santos, Paula Moura; Lago, Paula; Caetano, Cidalina; Rodrigues, Ângela; Portela, Francisco; Oliveira, Ana Catarina Lima de; Ministro, Paula; Cancela, Eugénia; Vieira, Ana Isabel; Barosa, Rita; Cotter, José; Carvalho, Pedro Boal; Cremers, Isabelle; Trabulo, Daniel; Caldeira, Paulo; Antunes, Artur; Rosa, Isadora; Moleiro, Joana; Peixe, Paula; Herculano, Rita; Gonçalves, Raquel; Gonçalves, Bruno; Sousa, Helena Tavares de; Contente, Luís; Morna, Henrique; Lopes, Susana; Magro, Fernando; Gedii, on behalf

doi:10.1093/ecco-jcc/jjw171

Cited by 17 publications

(28 citation statements)

References 25 publications

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“…Prior prediction models in CD have largely focused on estimating an individual patient’s probability of developing complications to help decide whether or not highly effective therapeutic approaches, such as early combined immunosuppression with a biologic, would be preferred strategies. 27–30 However, prediction models for treatment outcomes with biologic therapy in CD are limited. Two clinical models have been built for predicting primary non-response to TNFα-antagonists; however, performance of these models was limited by inadequate precision due to low event rates and sample sizes, and lack of external validation.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn’s Disease

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn’s Disease

et al. 2018

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“…Naïve Bayes (NB), which assumes conditional independence among factors, and Tree Augmented Naïve Bayes (TAN) [17], which allows for an optional dependence for each factor, were the Bayesian network classifiers used to build our models. They were chosen given their previous results in other clinical domains [11,25]. Four models were evaluated and compared, differing on the classifier (NB and TAN) and the number of predictive factors (38 or 6).…”

Section: Bayesian Networkmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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“…It will also be important to evaluate trends in health care use and adherence, determine risk of disease progression for individual patients, and predict response to therapy. These factors could help patients avoid ineffective therapies that increase the likelihood of poor outcomes (ie, surgery), [71][72][73] identify patients most likely to benefit from highly effective therapeutic interventions early in the disease course, [74][75][76][77] and created personalized decision support tools for patients at risk for high health care utilization and providers at risk for variability in care. 78 We have outlined the essential components of an effective PHM strategy and actionable items for health care systems to consider when implementing these strategies for IBD patients.…”

Section: Future Directionsmentioning

confidence: 99%

Population Health Management for Inflammatory Bowel Disease

et al. 2018

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Inflammatory bowel diseases (IBDs) are chronic and impose significant, multidimensional burdens on patients and health care systems. The increasing prevalence of IBD will only worsen this problem globally-population health management (PHM) strategies are needed to increase quality of care and population health outcomes while reducing health care costs. We discuss the key components of PHM in IBD. Effective implementation of PHM strategies requires accurate identification of at-risk patients and key areas of variability in care. Improving outcomes of the at-risk population requires implementation of a multicomponent chronic care model designed to shift delivery of ambulatory care from acute, episodic, and reactive encounters, to proactive, planned, long-term care. This is achieved through team care of an activated patient with the help of remote monitoring, clinical information systems, and integrated decision support, with accompanying changes in delivery systems. Performance measurement is integral to any PHM strategy. This involves developing and implementing meaningful metrics of different phases of quality of IBD care and measuring them efficiently using modern clinical information systems. Such an integrated framework of PHM in IBD will facilitate the delivery of high-value care to patients.

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Development and Validation of Risk Matrices for Crohn’s Disease Outcomes in Patients Who Underwent Early Therapeutic Interventions

Abstract: Clinical and demographical risk factors for disabling CD and reoperation were determined and their impact was quantified by means of risk matrices, which are applicable as bedside clinical tools that can help physicians during therapeutic decisions in early disease management.

Cited by 17 publications

References 25 publications

Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn’s Disease

Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn’s Disease

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Population Health Management for Inflammatory Bowel Disease

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