Matthew Thompson

Objective The objective of this study is to perform a comprehensive systematic review and meta-analysis of the accuracy of signs, symptoms and case definitions for the diagnosis of influenza. Design Systematic review and meta-analysis of diagnostic accuracy. Setting Inpatient or outpatient setting. Participants Three databases (PubMed, CINAHL and EMBASE) were searched through February 2024 for studies of clinical diagnosis of influenza using prospective data collection and a high-quality reference standard. Data were abstracted by researchers working in parallel and resolving discrepancies by discussion. Primary and secondary outcome measures Quality was assessed using QUADAS-2. Summary estimates (or ranges) of sensitivity and specificity, likelihood ratio (LR), the Youden Index and the area under the receiver operating characteristic curve were calculated. Results The final meta-analysis included 67 studies, each with between 119 and 155 866 participants. Most were judged to be low risk of bias. The signs and symptoms with the highest overall accuracy for all studies based on the Youden Index were any fever (0.32), overall clinical impression (0.28), coryza (0.25), cough and fever (0.25), and measured fever (0.25). Accuracy varied widely by age group. Only the overall clinical impression had a positive LR greater than 2.0. Cough was the most sensitive finding (0.92) with a negative LR of 0.28 in adults. The absence of any fever also had a low negative LR (0.30). The Centers for Disease Control and Prevention (CDC) definition of influenza-like illness (ILI) had good specificity but poor sensitivity in adults, while in infants, it had good sensitivity but widely varying specificity. The European CDC and WHO case definitions for ILI had modest sensitivity and specificity. Conclusions Individual signs and symptoms, their combinations, and ILI case definitions have very limited accuracy for identifying persons with influenza. More accurate surveillance and diagnosis will require the development and validation of accurate risk scores or greater use of point-of-care testing.

This viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources.

Objective This study has two objectives: first, to explore the diagnostic experiences of black/African American (BAA) patients with lung cancer to pinpoint pitfalls, suboptimal experiences and instances of discrimination leading to disparities in outcomes compared with patients of other ethnic backgrounds, especially white patients. The second objective is to identify the underlying causes contributing to health disparities in the diagnosis of lung cancer among BAA patients. Methods We employed a phenomenological research approach, guiding in-depth interviews with patients self-identifying as BAA diagnosed with lung cancer, as well as caregivers, healthcare professionals and community advocates knowledgeable about BAA experiences with lung cancer. We performed thematic analysis to identify experiences at patient, primary care and specialist levels. Contributing factors were identified using the National Institute of Minority Health and Health Disparities (NIMHD) health disparity model. Results From March to November 2021, we conducted individual interviews with 19 participants, including 9 patients/caregivers and 10 providers/advocates. Participants reported recurring and increased pain before seeking treatment, treatment for non-cancer illnesses, delays in diagnostic tests and referrals, poor communication and bias when dealing with specialists and primary care providers. Factors contributing to suboptimal experiences included reluctance by insurers to cover costs, provider unwillingness to conduct comprehensive testing, provider bias in recommending treatment, high healthcare costs, and lack of healthcare facilities and qualified staff to provide necessary support. However, some participants reported positive experiences due to their insurance, availability of services and having an empowered support structure. Conclusions BAA patients and caregivers encountered suboptimal experiences during their care. The NIMHD model is a useful framework to organise factors contributing to these experiences that may be leading to health disparities. Additional research is needed to fully capture the extent of these experiences and identify ways to improve BAA patient experiences in the lung cancer diagnosis pathway.

UNSTRUCTURED This viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources.

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.

Blood glucose measurement is commonly used to screen for and monitor diabetes, a chronic condition characterized by the inability to effectively modulate blood glucose that can lead to heart disease, vision loss, and kidney failure. Early detection of prediabetes can forestall or reverse more serious illness if healthy lifestyle adjustments or medical interventions are made in a timely manner. Current diabetes screening methods require visits to a healthcare facility and use of over-the-counter glucose-testing devices (glucometers), both of which are costly or inaccessible for many populations, reducing the chances of early disease detection. We therefore developed GlucoScreen, a readerless glucose test strip that enables affordable, single-use, at-home glucose testing, leveraging the user's touchscreen cellphone for reading and displaying results. By integrating minimal, low-cost electronics with commercially available blood glucose testing strips, the GlucoScreen prototype introduces a new type of low-cost, battery-free glucose testing tool that works with any smartphone, obviating the need to purchase a separate dedicated reader. Our key innovation is using the phone's capacitive touchscreen for the readout of the minimally modified commercially available glucose test strips. In an in vitro evaluation with artificial glucose solutions, we tested GlucoScreen with five different phones and compared the findings to two common glucometers, AccuChek and True Metrix. The mean absolute error (MAE) for our GlucoScreen prototype was 4.52 mg/dl (Accu-Chek test strips) and 3.7 mg/dl (True Metrix test strips), compared to 4.98 mg/dl and 5.44 mg/dl for the AccuChek glucometer and True Metrix glucometer, respectively. In a clinical investigation with 75 patients, GlucoScreen had a MAE of 10.47 mg/dl, while the AccuChek glucometer had a 9.88 mg/dl MAE. These results indicate that GlucoScreen's performance is comparable to that of commonly available over-the-counter blood glucose testing devices. With further development and validation, GlucoScreen has the potential to facilitate large-scale and lower cost diabetes screening. This work employs GlucoScreen's smartphone-based technology for glucose testing, but it could be extended to build other readerless electrochemical assays in the future.

Of the 39 752 patients included in the audit, less than half (41%) had blood tests done prior to diagnosis. Use of blood tests varied greatly depending on type of cancer. For example, patients with leukaemia (84%), myeloma (76%), or pancreatic cancers (71%) were far more likely to have blood tests than those with vulval cancer (8%), breast cancer (4%), or melanoma (2%). This provides strong evidence that GPs were judiciously considering the most appropriate diagnostic test for their patient with suspected (but at this point still undiagnosed) cancer. A greater proportion of patients presented with non-alarm (non-specific) symptoms (n = 16 487, 41%) than alarm symptoms (n = 13 778, 35%), as defined by the National Institute for Health and Care Excellence (NICE). Moreover, blood tests were nearly three times more likely to be used in those with non-alarm symptoms than those with alarm symptoms.

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.

Background: Unexpected weight loss is a presenting feature of cancer in primary care. Data from primary care are lacking to quantify how much weight loss over what period should trigger further investigation for cancer. This research aimed to quantify cancer diagnosis rates associated with measured weight change in people attending primary care.Methods: Retrospective cohort study of primary care electronic health records data linked to the Surveillance, Epidemiology, and End Results cancer registry (Integrated healthcare delivery system in Washington State, United States). Multivariable Cox regression incorporating time varying covariates using splines to model non‐linear associations (age, percentage weight change, and weight change interval). Fifty thousand randomly selected patients aged 40 years and over followed for up to 9 years (1 January 2006 to 31 December 2014). Outcome measures are hazard ratios (95% confidence intervals) to quantify the association between percentage weight change and cancer diagnosis for all cancers combined, individual cancer sites and stages; percentage risk of cancer diagnosis within 6 months of the end of each weight change episode; and the positive predictive value for cancer diagnosis.Results: There were 43 302 included in the analysis after exclusions. Over 287 858 patient‐years of follow‐up, including 24 272 (56.1%) females, 23 980 (55.4%) aged 40 to 59 years, 15 113 (34.9%) 60 to 79 years, and 4209 (9.7%) aged 80 years and over. Adjusted hazard ratios (95% confidence interval) for cancer diagnosis in a 60 years old ranged from 1.04 (1.02 to 1.05, P < 0.001) for 1% weight loss to 1.44 (1.23 to 1.68, P < 0.001) for 10%. An independent linear association was observed between percentage weight loss and increasing cancer risk. The absolute risk of cancer diagnosis increased with increasing age (up to 85 years) and as the weight change measurement interval decreased (<1 year). The positive predictive value for a cancer diagnosis within 1 year of ≥5% measured weight loss in a 60 to 69 years old was 3.41% (1.57% to 6.37%) in men and 3.47% (1.68% to 6.29%) in women. The risk of cancer diagnosis was significantly increased for pancreatic, myeloma, gastro‐oesophageal, colorectal, breast, stage II and IV cancers.Conclusions: Weight loss is a sign of undiagnosed cancer regardless of the interval over which it occurs. Guidelines should resist giving an arbitrary cut‐off for the interval of weight loss and focus on the percentage of weight loss and the patient's age. Future studies should focus on the association between diagnostic evaluation of weight change and risk of cancer mortality.