Predicting the diagnosis of inflammatory and autoimmune arthritis using a data driven approach.

一旦出现关节的特征性变化，例如强直性脊柱炎患者的骶髂关节融合，炎症性关节炎就很容易诊断。然而，特征性变化通常在损伤发生后出现，患者已经经历了大量的疼痛和损伤，并且在早期阶段阻止疾病的机会已经丧失。早期诊断意味着可以使用疾病修饰药物来减缓进展，并可能极大地改变疾病的进程或关闭它。这一点特别重要，因为功能丧失发生在疾病发作的头10年，而功能丧失是导致疾病造成的高成本的主要因素，包括失去工作和需要援助和照顾者。然而，在早期阶段，炎症性关节炎的许多症状是常见的老化，损伤或磨损，例如疲劳，关节疼痛，肿胀。这意味着他们很容易被病人和医生解雇，只需用止痛药和抗炎药治疗，当症状没有解决时，可能会出现长时间的延误。因此，有必要确定的GP可以使用的预测快速指导和转介患者专科治疗。然而，考虑到等待看专科医生的名单，预测因子具有高阳性预测值也很重要，这样就不会给医院系统带来额外负担，从而导致早期疾病诊断的更大延迟。在汇集常规收集的数据方面的新投资以及使用大型复杂数据集的模式检测方法的新发展意味着现在可以利用数据驱动方法来解决这个问题。到目前为止，这种方法一直是从二级保健医生那里获取临床知识，并将这些知识应用于他们认为应该出现在初级保健记录中的内容。本研究旨在采用数据驱动的方法，并检查极早期自身免疫性炎症性关节炎的早期预测因子。早期预测因素可能是所有常见的症状，如感染，抗生素使用，抗炎药和止痛药的多种处方，全科医生建议的下班时间，疲劳和发烧。然而，这些常见症状的模式和它们相对于彼此发生的时间可能是主要的预测因素。

Inflammatory arthritis is easy to diagnose once the characteristic changes in the joints is present, for example fusion of the sacroiliac joints for those with ankylosing spondylitis. However, the characteristic changes are normally present after damage has been done, the person has already experienced a great deal of pain and damage and the window of opportunity to stop the disease in the early stages has been lost. An early diagnosis means that disease modifying drugs can be used to slow the progression and possibly greatly change the course of the disease or switch it off. This is especially important as the loss of function occurs in the first 10 years of disease onset and loss of function is the main factor leading to high costs of disease in terms of loss of work and need for assistance and a carer. However, in the early stages many symptoms of inflammatory arthritis are common to aging, injury or wear and tear, for example fatigue, joint pain, swelling. This means they can easily be dismissed by the patient and the doctor and simply treated with pain killers and anti-inflammatory and long delays can occur when symptoms do not resolve. Thus, there is a need to identify predictors that can be used by a GP to rapidly direct and refer patients to specialist treatment. However, considering the waiting lists to see a specialist, it is also important that the predictors have a high positive predictive value so that extra burden is not placed on the hospital system causing greater delays in diagnosis of early disease. New investments in bringing together routinely collected data and new developments in pattern detection methods using large complex datasets means that it is now possible to take advantage of a data driven method approach to tackle this problem. Until now the approach has been taking clinical knowledge from secondary care physicians and applying this knowledge to what they think should be present in primary care records. This study aims to take a data driven approach and to examine early predictors of very early autoimmune inflammatory arthritis. It is possible that the early predictors are all common symptoms such as an infection, antibiotic use, multiple prescriptions for anti-inflammatory drugs and pain killers, time off work as recommended by GP, fatigue and fever. However, the pattern of these common symptoms and timing of when they occur relative to each other, may be the main predictive factor.