{
  "abstract": "Objectives To develop predictive models for early and overall tuberculosis (TB) deaths for prospective use at TB diagnosis in resource-constrained TB programme settings.Design Statewide cohort study using routinely captured secondary data.Setting With the majority of TB deaths being early (within 2 months), India’s TB programme’s information management system ( Ni-kshay)-dependent death prediction models (using age, gender, TB site, previous treatment, microbiological confirmation, HIV, diabetes and bank account availability) are not feasible for prospective use, as few variables are captured at diagnosis. Utilising routinely captured triage variables for severe illness at diagnosis (body mass index, pedal oedema, respiratory rate, oxygen saturation and ability to stand without support) from an ongoing statewide and state-specific differentiated TB care initiative to reduce TB deaths in Tamil Nadu state (southern India, 80 million population with 0.1 million annual notifications), robust models for prospective use were developed.Participants Adults (aged ≥15 years) with TB (not known to be drug-resistant at diagnosis) that were notified from public facilities of Tamil Nadu from July 2022 to June 2023.Outcome measures Early and overall (within 12 months of notification) TB deaths. Area under the receiver operating characteristic curve (AUC) was used to assess accuracy of models built using modified Poisson regression.Results Among 55 971 adults, the overall death rate was 7.4%, and 67.9% of the deaths were early. In predicting overall deaths, accuracy of the model using all Ni-kshay variables (AUC 0.716 (95% CI 0.707 to 0.725)) was as good as the model using triage variables for severe illness only (AUC 0.701 (95% CI 0.691 to 0.711)). To the latter, adding potentially capturable Ni-kshay variables at diagnosis (age, gender, TB site, previous treatment and microbiological confirmation) significantly improved model accuracy (AUC 0.754 (95% CI 0.745 to 0.763)). Further addition of remaining Ni-kshay variables did not improve accuracy significantly. Death prediction equations were generated for these models.Conclusion Simple and easily measurable triage variables for severe illness should be routinely captured at TB diagnosis. A death prediction calculator ( http://44.208.93.99/) based on these variables (specifically triage variables for severe illness combined with age, gender, TB site, previous treatment and microbiological confirmation) may be used by Indian states and high TB burden countries seeking scalable, data-driven interventions to reduce TB deaths.",
  "authors": [
    {
      "affiliations": [
        "Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Suseendar Shanmugasundaram"
    },
    {
      "affiliations": [
        "Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Hemant Deepak Shewade"
    },
    {
      "affiliations": [
        "Infectious Disease Epidemiology, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Ramalingam Srinivasan"
    },
    {
      "affiliations": [
        "State TB Office, Government of Tamil Nadu, Chennai, India"
      ],
      "name": "Asha Frederick"
    },
    {
      "affiliations": [
        "Computing and Information Sciences, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Ramasamy Sabarinathan"
    },
    {
      "affiliations": [
        "ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Premkumar Harish"
    },
    {
      "affiliations": [
        "S.A. Engineering College, Chennai, Chennai, India"
      ],
      "name": "Deepan Balu"
    },
    {
      "affiliations": [
        "Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Jeen M Melfha"
    },
    {
      "affiliations": [
        "School of Public Health, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Kumari Gayathri"
    },
    {
      "affiliations": [
        "Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Radhakrishnan Vijayaprabha"
    },
    {
      "affiliations": [
        "School of Public Health, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Annadurai Jeyakumar"
    },
    {
      "affiliations": [
        "Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Dilsadh Kabir"
    },
    {
      "affiliations": [
        "State TB Office, Government of Tamil Nadu, Chennai, India"
      ],
      "name": "Meikappan Eraivan"
    },
    {
      "affiliations": [
        "School of Public Health, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Tarun Bhatnagar"
    },
    {
      "affiliations": [
        "Infectious Disease Epidemiology, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India"
      ],
      "name": "Manoj V Murhekar"
    }
  ],
  "title": "Tuberculosis death prediction calculator for prospective use at diagnosis in resource-constrained programme settings: a statewide cohort study",
  "uid": "f1ce11d5-8d77-521b-84b1-bb9cc0d9a159"
}