Association of Weight Loss in Ambulatory Care Settings With First Diagnosis of Lung Cancer in the US.

Importance: Lung cancer, the US's leading cause of cancer death, is often diagnosed following presentation to health care settings with symptoms, and many patients present with late-stage disease. Objective: To investigate the association between weight loss and subsequent diagnosis of incident lung cancer in an ambulatory care population and to assess whether recorded weight change had higher odds of lung cancer diagnosis than objective measurements. Design, Setting, and Participants: This case-control study included patients visiting a US academic medical center between January 1, 2012, and December 31, 2019. Data were derived from US ambulatory care electronic health records from the University of Washington Medical Center linked to the local Surveillance, Epidemiology, and End Results cancer registry. Cases were identified from patients who had a primary lung cancer diagnosis between 2012 and 2019; controls were matched on age, sex, smoking status, and presenting to the same type of ambulatory clinic as cases. Data were analyzed from March 2022 through January 2023. Exposure: Continuous and categorical weight change were assessed. Main Outcomes and Measures: Odds ratios estimating the likelihood of a diagnosis of lung cancer were calculated using univariable and multivariable conditional logistic regression. Results: A total of 625 patients aged 40 years or older with a first primary lung cancer diagnosis and 4606 matched controls were included (1915 [36.6%] ages 60 to 69 years; 418 [8.0%] Asian, 389 [7.4%] Black, 4092 [78.2%] White). In unadjusted analyses, participants with weight loss of 1% to 3% (odds ratio [OR], 1.12; 95% CI, 0.88-1.41), 3% to 5% (OR, 1.36; 95% CI, 0.99-1.88), or 5% to 10% (OR, 1.23; 95% CI, 0.82-1.85) over a 2-year period did not have statistically significantly increased risk of lung cancer diagnosis compared with those who maintained a steady weight. However, participants with weight loss of 10% to 50% had more than twice the odds of a lung cancer diagnosis (OR, 2.27; 95% CI, 1.27-4.05). Most categories of weight loss showed significant associations with an increased risk of lung cancer diagnosis for at least 6 months prior to diagnosis. Patients who had weight loss both recorded in clinicians' notes and measured had higher odds of lung cancer compared with patients who had only recorded (OR, 1.26; odds; 95% CI, 1.04-1.52) or measured (OR, 8.53; 95% CI, 6.99-10.40) weight loss. Conclusions and Relevance: In this case-control study, weight loss in the prior 6 months was associated with incident lung cancer diagnosis and was present whether weight loss was recorded as a symptom by the clinician or based on changes in routinely measured weight, demonstrating a potential opportunity for early diagnosis. The association between measured and recorded weight loss by clinicians presents novel results for the US.

Symptoms and signs of lung cancer prior to diagnosis: case-control study using electronic health records from ambulatory care within a large US-based tertiary care centre.

OBJECTIVE: Lung cancer is the most common cause of cancer-related death in the USA. While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHRs) in the USA. We aimed to identify symptoms and signs in patients prior to diagnosis in EHR data. DESIGN: Case-control study. SETTING: Ambulatory care clinics at a large tertiary care academic health centre in the USA. PARTICIPANTS, OUTCOMES: We studied 698 primary lung cancer cases in adults diagnosed between 1 January 2012 and 31 December 2019, and 6841 controls matched by age, sex, smoking status and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariable conditional logistic regression were used to identify symptoms and signs associated with lung cancer at time of diagnosis, and 1, 3, 6 and 12 months before the diagnosis/index dates. RESULTS: Eleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariable analyses. Of these, seven were significantly associated with lung cancer 6 months prior to diagnosis: haemoptysis (OR 3.2, 95% CI 1.9 to 5.3), cough (OR 3.1, 95% CI 2.4 to 4.0), chest crackles or wheeze (OR 3.1, 95% CI 2.3 to 4.1), bone pain (OR 2.7, 95% CI 2.1 to 3.6), back pain (OR 2.5, 95% CI 1.9 to 3.2), weight loss (OR 2.1, 95% CI 1.5 to 2.8) and fatigue (OR 1.6, 95% CI 1.3 to 2.1). CONCLUSIONS: Patients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often 6 months or more before diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer.

How Timely Is Diagnosis of Lung Cancer? Cohort Study of Individuals with Lung Cancer Presenting in Ambulatory Care in the United States.

The diagnosis of lung cancer in ambulatory settings is often challenging due to non-specific clinical presentation, but there are currently no clinical quality measures (CQMs) in the United States used to identify areas for practice improvement in diagnosis. We describe the pre-diagnostic time intervals among a retrospective cohort of 711 patients identified with primary lung cancer from 2012-2019 from ambulatory care clinics in Seattle, Washington USA. Electronic health record data were extracted for two years prior to diagnosis, and Natural Language Processing (NLP) applied to identify symptoms/signs from free text clinical fields. Time points were defined for initial symptomatic presentation, chest imaging, specialist consultation, diagnostic confirmation, and treatment initiation. Median and interquartile ranges (IQR) were calculated for intervals spanning these time points. The mean age of the cohort was 67.3 years, 54.1% had Stage III or IV disease and the majority were diagnosed after clinical presentation (94.5%) rather than screening (5.5%). Median intervals from first recorded symptoms/signs to diagnosis was 570 days (IQR 273-691), from chest CT or chest X-ray imaging to diagnosis 43 days (IQR 11-240), specialist consultation to diagnosis 72 days (IQR 13-456), and from diagnosis to treatment initiation 7 days (IQR 0-36). Symptoms/signs associated with lung cancer can be identified over a year prior to diagnosis using NLP, highlighting the need for CQMs to improve timeliness of diagnosis.

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications

The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform better, while being faster to train, more re-usable, and significantly simpler.