Integrating tobacco treatment into lung cancer screening practices: Study protocol for the Screen ASSIST randomized clinical trial.

BACKGROUND: Integrating tobacco treatment services into lung cancer screening (LCS) has the potential to leverage a 'teachable moment' to promote cessation among long-term smokers and reduce disparities in tobacco treatment access. This protocol paper describes the Screen ASSIST (Aiding Screening Support In Stopping Tobacco) trial, which will identify how to best deliver evidence-driven tobacco treatment in the context of LCS. METHODS: Screen ASSIST is a randomized clinical trial with a 3-factor, fully crossed factorial design that enrolls current smokers (any cigarette use in the past 30 days) scheduled to attend LCS at multiple sites in the Mass General Brigham healthcare system. To maximize reach, recruitment is conducted at 3 time points: 1) at the time of LCS scheduling, 2) at the LCS visit, and 3) after the participant has received their LCS results. Participants are stratified by LCS study site and recruitment point and randomly assigned into 8 groups that test intervention components varying on telehealth counseling duration (4 weeks vs. 8 weeks), nicotine replacement therapy duration (2 weeks vs. 8 weeks), and systematic screening and referral for social determinants of health via a service named 'AuntBertha' (referral vs. no referral). The primary study outcome is self-reported past 7-day tobacco abstinence at 6-month follow-up. This trial will also assess systems integration and evaluate implementation of the intervention. DISCUSSION: Screen ASSIST will identify the most effective combination of tobacco cessation treatments within the LCS context, in order to improve the cost-effectiveness of LCS and quality of life among long-term heavy smokers.

Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records.

OBJECTIVES: UK statistics suggest only two-thirds of patients with dementia get a diagnosis recorded in primary care. General practitioners (GPs) report barriers to formally diagnosing dementia, so some patients may be known by GPs to have dementia but may be missing a diagnosis in their patient record. We aimed to produce a method to identify these 'known but unlabelled' patients with dementia using data from primary care patient records. DESIGN: Retrospective case-control study using routinely collected primary care patient records from Clinical Practice Research Datalink. SETTING: UK general practice. PARTICIPANTS: English patients aged >65 years, with a coded diagnosis of dementia recorded in 2000-2012 (cases), matched 1:1 with patients with no diagnosis code for dementia (controls). INTERVENTIONS: Eight coded and nine keyword concepts indicating symptoms, screening tests, referrals and care for dementia recorded in the 5 years before diagnosis. We trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, random forest). PRIMARY AND SECONDARY OUTCOMES: The outcome variable was dementia diagnosis code; the accuracy of classifiers was assessed using area under the receiver operating characteristic curve (AUC); the order of features contributing to discrimination was examined. RESULTS: 93 426 patients were included; the median age was 83 years (64.8% women). Three classifiers achieved high discrimination and performed very similarly. AUCs were 0.87-0.90 with coded variables, rising to 0.90-0.94 with keywords added. Feature prioritisation was different for each classifier; commonly prioritised features were Alzheimer's prescription, dementia annual review, memory loss and dementia keywords. CONCLUSIONS: It is possible to detect patients with dementia who are known to GPs but unlabelled with a diagnostic code, with a high degree of accuracy in electronic primary care record data. Using keywords from clinic notes and letters improves accuracy compared with coded data alone. This approach could improve identification of dementia cases for record-keeping, service planning and delivery of good quality care.

The 4Ts scoring system for heparin-induced thrombocytopenia in medical-surgical intensive care unit patients.

BACKGROUND: Heparin-induced thrombocytopenia (HIT) is commonly considered but rarely confirmed in critically ill patients. The 4Ts score (Thrombocytopenia, Timing of thrombocytopenia, Thrombosis, and oTher reason) might identify individual patients at risk of having this disorder. OBJECTIVE: The aim of the study was to evaluate the value of the 4Ts HIT score in comparison with the serotonin-release assay (SRA) in critically ill patients. METHODS: This study describes the combined results of 3 prospective studies enrolling critically ill patients who were investigated for HIT if platelets fell to less than 50 x 10(9)/L or if platelet counts decreased to less than 50% of the value upon intensive care unit admission. We confirmed HIT by a positive platelet SRA. We assigned a 4Ts score blinded to SRA results to all 50 patients investigated for HIT; those with positive SRA results were scored in duplicate. RESULTS: Of 528 patients, 50 (9.5%) were investigated for HIT; 39 (78%) of 50 (64%-88%) of these patients were scored as "low probability" by 4Ts score and none had a positive SRA. Of 49 patients who underwent SRA testing because of thrombocytopenia, only 2 (4.1%; 0.5-14.0) had a positive SRA (1 with a moderate 4Ts score and 1 with a high 4Ts score). Therefore, the overall incidence of HIT confirmed by SRA was 2 (0.4%) of 528 (0.04%-1.4%). CONCLUSIONS: Significant thrombocytopenia during heparin administration occurred in 9.5% of critically ill patients, but HIT was confirmed in only 4.1% of those undergoing testing, for an overall incidence of 0.4%. A low 4Ts score occurred in 78% of patients investigated for HIT; none of these patients had a positive SRA. We conclude that HIT is uncommon in critically ill patients and that the 4Ts score is worthy of further evaluation in this patient population.