Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs

A Structured Community Engagement Strategy to Support Uptake of TB Active Case-finding

Background: In Lima, Peru, a mobile TB screening program (“TB Móvil”) was implemented in high TB prevalence districts to increase TB screening. Community engagement activities to promote TB Móvil were simultaneously conducted. Objective: To describe a structured, theory-driven community engagement strategy to support the uptake of TB Móvil. Methods: We adapted Popular Opinion Leader (POL), an evidence-based social networking intervention previously used in Peru to promote HIV testing, for TB Móvil. Community health workers, women who run soup kitchens, and motorcycle taxi drivers served as “popular opinion leaders” who disseminated information about TB Móvil in everyday conversations, aided by a multi-media campaign. Performance indicators of POL included the number/characteristics of persons screened; number of multimedia elements; and proportion of persons with abnormal radiographs hearing about TB Móvil before attending. Results: Between February 2019 and January 2020, 63,899 people attended the TB Móvil program at 210 sites; 60.1% were female. The multimedia campaign included 36 videos, 16 audio vignettes, flyers, posters, community murals and “jingles.” Among attendees receiving an abnormal chest X-ray suggestive of TB, 48% (6,935/14,563) reported hearing about TB Móvil before attending. Conclusion: POL promotes the uptake of TB Móvil and should be considered as a strategy for increasing TB screening uptake

A Role for Community-level Socioeconomic indicators in Targeting Tuberculosis Screening Interventions

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019–February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58–0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs \u3e 83.1%; OR: 1.7; 95% CI: 1.2–2.6) and the percent of TB patients with a prior tuberculosis episode (\u3e 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0–12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions

A Role for Community-level Socioeconomic indicators in Targeting Tuberculosis Screening Interventions

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019–February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58–0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs \u3e 83.1%; OR: 1.7; 95% CI: 1.2–2.6) and the percent of TB patients with a prior tuberculosis episode (\u3e 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0–12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions

Reporting checklist for diagnostic test accuracy studies.

Reporting checklist for diagnostic test accuracy studies.</p

Summary of diagnostic accuracy for qXR version 4 using the culture (primary) and Xpert (secondary) reference standards in the triage and screening cohorts.

Summary of diagnostic accuracy for qXR version 4 using the culture (primary) and Xpert (secondary) reference standards in the triage and screening cohorts.</p

Summary of diagnostic accuracy for qXR version 3 compared to the culture (primary) and Xpert (secondary) reference standards.

Summary of diagnostic accuracy for qXR version 3 compared to the culture (primary) and Xpert (secondary) reference standards.</p

Sensitivity of qXR version 4 for culture-confirmed pulmonary tuberculosis, overall and in pre-specified stratified groups.

Sensitivity of qXR version 4 for culture-confirmed pulmonary tuberculosis, overall and in pre-specified stratified groups.</p

S1 Table -

a: Summary of Culture and Xpert results concordance. b: Xpert sensitivity for smear-positive and smear-negative culture-positive TB in triage cohort patients. (DOCX)</p

Identifying Barriers and Facilitators to Implementation of Community-based Tuberculosis Active Case Finding with Mobile X-ray Units in Lima, Peru: a RE-AIM Evaluation

Objectives: Identify barriers and facilitators to integrating community tuberculosis screening with mobile X-ray units into a health system. Methods: Reach, effectiveness, adoption, implementation and maintenance evaluation. Setting: 3-district region of Lima, Peru. Participants: 63 899 people attended the mobile units from 7 February 2019 to 6 February 2020. Interventions: Participants were screened by chest radiography, which was scored for abnormality by computer-aided detection. People with abnormal X-rays were evaluated clinically and by GeneXpert MTB/RIF (Xpert) sputum testing. People diagnosed with tuberculosis at the mobile unit were accompanied to health facilities for treatment initiation. Primary and secondary outcome measures: Reach was defined as the percentage of the population of the three-district region that attended the mobile units. Effectiveness was defined as the change in tuberculosis case notifications over a historical baseline. Key implementation fidelity indicators were the percentages of people who had chest radiography performed, were evaluated clinically, had sputum samples collected, had valid Xpert results and initiated treatment. Results: The intervention reached 6% of the target population and was associated with an 11% (95% CI 6 to 16) increase in quarterly case notifications, adjusting for the increasing trend in notifications over the previous 3 years. Implementation indicators for screening, sputum collection and Xpert testing procedures all exceeded 85%. Only 82% of people diagnosed with tuberculosis at the mobile units received treatment; people with negative or trace Xpert results were less likely to receive treatment. Suboptimal treatment initiation was driven by health facility doctors’ lack of familiarity with Xpert and lack of confidence in diagnoses made at the mobile unit. Conclusion: Mobile X-ray units were a feasible and effective strategy to extend tuberculosis diagnostic services into communities and improve early case detection. Effective deployment however requires advance coordination among stakeholders and targeted provider training to ensure that people diagnosed with tuberculosis by new modalities receive prompt treatment