Development of screening tools to predict the risk of recurrence and related complications following anal fistula surgery: protocol for a prospective cohort study

IntroductionPostoperative recurrence and related complications are common and related to poor outcomes in patients with anal fistula (AF). Due to being associated with short-term and long-term cure rates, perioperative complications have received widespread attention following AF surgery. This study aims to identify a set of predictive factors to develop risk prediction models for recurrence and related complications following AF surgery. We plan to develop and validate risk prediction models, using information collected through a WeChat patient-reported questionnaire system combined with clinical, laboratory and imaging findings from the perioperative period until 3–6 months following AF surgery.Methods and analysisThis is a prospective hospital-based cohort study using a linked database of collected health data as well as the follow-up outcomes for all adult patients who suffered from AF at a tertiary referral hospital in Shanghai, China. We will perform logistic regression models to predict anal fistula recurrence (AFR) as well as related complications (eg, wound haemorrhage, faecal impaction, urinary retention, delayed wound healing and unplanned hospitalisation) during and after AF surgery, and machine learning approaches will also be applied to develop risk prediction models. This prospective study aims to develop the first risk prediction models for AFR and related complications using multidimensional variables. These tools can be used to warn, motivate and empower patients to avoid some modifiable risk factors to prevent postoperative complications early. This study will also provide alternative tools for the early screening of high-risk patients with AFR and related complications, helping surgeons better understand the aetiology and outcomes of AF in an earlier stage.Ethics and disseminationThe study was approved by the Institutional Review Board of Shuguang Hospital affiliated with Shanghai University of Traditional Chinese Medicine (approval number: 2019-699-54-01). The results of this study will be submitted to international scientific peer-reviewed journals or conferences in surgery, anorectal surgery or anorectal diseases.Trial registration numberChiCTR1900025069; Pre-results.

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Patient-Reported Symptoms Improve Performance of Risk Prediction Models for Emergency Department Visits Among Patients With Cancer: A Population-Wide Study in Ontario Using Administrative Data

Journal of Pain and Symptom Management ◽

10.1016/j.jpainsymman.2019.07.007 ◽

2019 ◽

Vol 58 (5) ◽

pp. 745-755 ◽

Cited By ~ 6

Author(s):

Rinku Sutradhar ◽

Mehdi Rostami ◽

Lisa Barbera

Keyword(s):

Emergency Department ◽

Risk Prediction ◽

Administrative Data ◽

Prediction Models ◽

Emergency Department Visits ◽

Improve Performance ◽

Risk Prediction Models ◽

Patients With Cancer ◽

Patient Reported

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Patient-Reported Outcomes Improves the Prediction of In-patient and Emergency Department Readmission Risks in Coronary Artery Disease

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.716 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Danielle Southern ◽

Colleen Norris ◽

Hude Quan ◽

Maria Santana ◽

Matthew James ◽

...

Keyword(s):

Emergency Department ◽

Risk Prediction ◽

Prediction Models ◽

Policy Decision ◽

Patient Reported Outcome Measures ◽

Baseline Survey ◽

Post Discharge ◽

Risk Prediction Models ◽

Patient Reported ◽

Artery Disease

IntroductionCoronary Artery Disease (CAD) patients are known to report higher healthcare resource use, such as inpatient [IP] and emergency department [ED] readmissions, than the general population. We investigate if the patient reported outcome measures (PROMs) improve the accuracy of readmissions risk prediction models in CAD. Objectives and ApproachPatients enrolled in the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry between 1995 and 2014 who received catheterization (CATH) and completed baseline PROMs were linked to discharge abstract data and national ambulatory data. Logistic regression (LR) was used to develop 30-day and 1-year readmissions risk prediction models adjusting for patients’ demographic, clinical, and self-reported characteristics. PROM was measured using the 19-item Seattle Angina Questionnaire (SAQ). The discriminatory performance of each prediction model was assessed using the Harrel’s c-statistic for LR. ResultsOf the 13,264 patients who completed baseline SAQ, 59 (0.3%) had IP readmissions or ED visits within 30 days, and up to 356 (1.9%) within 1 year of baseline survey. The C-statistics for one-year readmissions risk prediction models that only adjusted for demographic and clinical variables only ranged between 56.4% and 61.2%. The prognostic improvement in the discrimination of these models ranged between 2% to 10% when patient-reported SAQ was included as predictor. The addition of SAQ improves the model discrimination in all types of admission. Conclusion/ImplicationsThe addition of PROMs improves the moderate accuracy of readmissions risk prediction models. These findings highlight the need for routine collection of PROMs in clinical settings and their potential use for aiding clinical and policy decision-making and post-discharge outcomes monitoring in the management of cardiovascular diseases.

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