A Clinical Risk Score for Early Management of TB in Uganda (PredicTB)

PredicTB: Validating a Clinical Risk Score for Early Management of Tuberculosis in Ugandan Primary Health Clinics

Although curative treatment exists, tuberculosis (TB) remains the leading cause of infectious mortality worldwide - often because people seek care for TB symptoms in highly resource-constrained clinics that cannot provide same-day diagnostic testing. The research team has developed an easy-to-use clinical risk score that, if implemented in these settings, might help clinicians identify patients at high risk for TB and thereby start treatment for those patients on the same day. This study will investigate the effectiveness and implementation of this score in four peri-urban clinics in Uganda, providing critical pragmatic data to inform (or halt) the design of a definitive large-scale cluster randomized trial.

Study Overview

• Status: Completed
• Conditions: Tuberculosis, Pulmonary
• Intervention / Treatment: Other: PredicTB score

Detailed Description

An estimated 1.5 million people die of tuberculosis (TB) every year. Many of these are people who seek care in under-resourced clinics (for example, in rural areas or informal settlements) where same-day TB diagnosis is not available. These patients are often unable to return promptly to receive their results and start treatment, resulting in ongoing disease transmission and often death. If TB treatment could be started on the same day as these patients initially seek care, substantial mortality and transmission could be averted. The research team has developed and validated a clinical risk score ("PredicTB") for adult pulmonary TB that could aid in clinical decision-making. This risk score ranges from 1-10, can be calculated by hand in under a minute using readily available clinical data (e.g., age, sex, self-reported HIV status), and has sufficiently high accuracy to inform decisions about same-day empiric treatment initiation while confirmatory test results are pending. Same-day treatment initiation improves patient outcomes for other infectious diseases (for example, sexually transmitted diseases including HIV), and this novel clinical risk score holds similar promise for TB, the leading cause of infectious mortality worldwide. However, before conducting a large-scale cluster randomized trial to evaluate whether this score could improve patient-important outcomes, it is critical to first generate evidence that this score could be effective and be implemented in the most-resource-limited settings for which it is intended.

The research team proposes a type 2 hybrid effectiveness-implementation evaluation of the PredicTB clinical risk score in four peri-urban clinics in Uganda, with an additional four clinics serving as a comparison group. The Specific Aims are to evaluate the effectiveness of PredicTB on clinical outcomes including rapid treatment initiation, TB mortality, and loss to care (Aim 1); to evaluate the implementation of PredicTB in terms of reach, adoption, implementation, and maintenance (Aim 2); and the project the long-term impact and cost-effectiveness of PredicTB implementation (Aim 3). The primary outcome is the increase in the proportion of patients with microbiologically confirmed TB who start treatment within seven days of initial presentation. To accomplish these aims, the research team will adopt a highly pragmatic study design in which the research team train clinicians in the use of the PredicTB score and perform quarterly site visits but otherwise minimize contact between study staff and treating clinicians. This will enable the research team to evaluate whether implementation of PredicTB is likely to impact clinical decision-making and patient outcomes under actual field settings. If successful, this evaluation will provide critical data to justify (or halt) the conduct of a large-scale pragmatic clinical trial - not only will it generate preliminary evidence of effectiveness, but it will also inform appropriate implementation. Patients in highly resource-constrained settings are at the greatest risk of suffering the ill effects of TB disease, including long-term morbidity and death. This study represents an important first step toward improving clinical management for these marginalized patients and thus toward reaching global targets for ending the TB epidemic.

• Study Type: Interventional
• Enrollment (Actual): 3332
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Uganda
  • Kampala, Uganda
    • Makerere University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 15 years and older (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• All adult patients submitting sputum for a new diagnosis of pulmonary TB in the four study clinics and four comparison clinics between month -6 and month 18 will have their records abstracted by study staff.
• Starting in the 13th month after PredicTB implementation (i.e., after the 12-month post-implementation period has ended), study staff will position themselves in the four study clinics for purposes of recruiting and enrolling adult patients submitting sputum for a new diagnosis of pulmonary TB. No exclusions will be made except for age (as above), and we will seek to enroll all consecutive patients until our target sample size (25 participants per clinic, total n = 100) has been reached.

Exclusion Criteria:

• Age < 15 years old

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Score intervention arm; The PredicTB score will be implemented in this arm.	Other: PredicTB score; This is an easy-to-use clinical risk score designed to improve early management of tuberculosis in highly resource-constrained settings where same-day microbiological testing is unavailable. It consists of readily accessible demographic and clinical data and is scored from 1-10. We will train clinic staff in eight clinics (four study clinics and four comparison clinics) on the Ugandan standard of care for the diagnosis and treatment of TB. In addition, in the four study clinics, we will provide training on the PredicTB score.
No Intervention: Control arm; The standard of care will be conducted in this arm.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Difference in 7-day Treatment Initiation From Pre-implementation to Post-implementation	The percentage of participants with microbiologically confirmed TB who initiated treatment within 7 days during post-implementation 'minus' The percentage of participants with microbiologically confirmed TB who initiated treatment within 7 days during pre-implementation	Up to 12 months post intervention
Implementation: Percentage of Encountered Patients at Intervention Arm Who Initiated the Same-day Treatment Based on PredicTB Score as Indicated	Percentage of patients who initiated same-day treatment divided by the number of patients who had a higher PredicTB score than the clinic-specific score of treatment threshold in the post-implementation period in intervention arm	Up to 12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incremental Cost-effectiveness of PredicTB	(cost of implementing PredicTB - cost of standard of care)/(projected disability-adjusted life years (DALYs) in standard of care - projected DALYs with PredicTB)	Months 0 - 12
Difference in TB Mortality From Pre-implementation to Post-implementation	The percentage of participants with microbiologically confirmed TB who died of any cause in the post-implementation "minus" The percentage of participants with microbiologically confirmed TB who died of any cause in the pre-implementation	12 Months
Difference in Loss to Care From Pre-implementation To Post-implementation	The percentage of participants with microbiologically confirmed TB who were lost to follow-up in the post-implementation "minus" The percentage of participants with microbiologically confirmed TB who were lost to follow-up in the pre-implementation	12 Months
Difference in Percentage of Participants With Microbiologically Confirmed TB	Difference in the percentage of participants with microbiologically confirmed TB who initiated treatment within 7 days from post-implementation to pre-implementation at intervention arm "minus" Difference in the percentage of participants with microbiologically confirmed TB who initiated treatment within 7 days from post-implementation to pre-implementation at comparison arm	Up to 12 months post-implementation
Reach: Percentage of Patients Who Were Administered (or Evaluated) by PredicTB Score	Percentage of patients who were administered (or evaluated) by PredicTB score among those who presented presumptive TB symptoms at clinics in the post-implementation period in intervention arm	Up to 12 months
Adoption: Percentage of Providers Adopting PredicTB	Percentage of providers using PredicTB in over 50% of encounters in which sputum is submitted for pulmonary TB diagnosis among those seeing >5 patients who submit sputum for diagnosis of pulmonary TB	Month 18
Maintenance: Change in Effectiveness Over Time in the Post-implementation Phase at Intervention Arm	Percentage of participants with microbiologically confirmed TB who initiated treatment within seven days in the post-implementation phase at intervention arm "minus" Percentage of participants with microbiologically confirmed TB who initiated treatment within seven days in the post-implementation phase at intervention arm	Up to 12 months
Modeled Changes in 5-year Mortality With PredicTB	Modeled hypothetical, expected changes in mortality at year 5, comparing simulations in which PredicTB is implemented to those in which PredicTB is not implemented, using a Markov state-transition model	Month 12

Collaborators and Investigators

• Sponsor: Johns Hopkins Bloomberg School of Public Health
• Collaborators: National Institute of Allergy and Infectious Diseases (NIAID)
• Investigators: Principal Investigator: David W Dowdy, MD/PHD, Johns Hopkins Bloomberg School of Public Health

Study record dates

Study Major Dates

• Study Start (Actual): November 10, 2021
• Primary Completion (Actual): July 15, 2023
• Study Completion (Actual): February 29, 2024

Study Registration Dates

• First Submitted: November 1, 2021
• First Submitted That Met QC Criteria: November 12, 2021
• First Posted (Actual): November 17, 2021

Study Record Updates

• Last Update Posted (Actual): July 23, 2024
• Last Update Submitted That Met QC Criteria: June 28, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Lung Diseases; Bacterial Infections; Bacterial Infections and Mycoses; Gram-Positive Bacterial Infections; Actinomycetales Infections; Mycobacterium Infections; Tuberculosis; Tuberculosis, Pulmonary
• Other Study ID Numbers: R21AI161301 (U.S. NIH Grant/Contract); IRB00015699 (Other Identifier: JHSPH IRB)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No