Dataset Information

Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.

ABSTRACT:

Background

Development of a prediction model using baseline characteristics of tuberculosis (TB) patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring model for predicting the death among newly diagnosed drug sensitive pulmonary TB patients in South India.

Methods

We undertook a longitudinal analysis of cohort data under the Regional Prospective Observational Research for Tuberculosis India consortium. Multivariable cox regression using the stepwise backward elimination procedure was used to select variables for the model building and the nomogram-scoring system was developed with the final selected model.

Results

In total, 54 (4.6%) out of the 1181 patients had died during the 1-year follow-up period. The TB mortality rate was 0.20 per 1000 person-days. Eight variables (age, gender, functional limitation, anemia, leukopenia, thrombocytopenia, diabetes, neutrophil-lymphocyte ratio) were selected and a nomogram was built using these variables. The discriminatory power was 0.81 (95% confidence interval: 0.75-0.86) and this model was well-calibrated. Decision curve analysis showed that the model is beneficial at a threshold probability ~15-65%.

Conclusions

This scoring system could help the clinicians and policy makers to devise targeted interventions and in turn reduce the TB mortality in India.

SUBMITTER: Krishnamoorthy Y

PROVIDER: S-EPMC10273380 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.

Krishnamoorthy Yuvaraj Y Ezhumalai Komala K Murali Sharan S Rajaa Sathish S Majella Marie Gilbert MG Sarkar Sonali S Lakshminarayanan Subitha S Joseph Noyal Mariya NM Soundappan Govindarajan G Prakash Babu Senbagavalli S Horsburgh Charles C Hochberg Natasha N Johnson W Evan WE Knudsen Selby S Pentakota Sri Ram SR Salgame Padmini P Roy Gautam G Ellner Jerrold J

Journal of public health (Oxford, England) 20230601 2

<h4>Background</h4>Development of a prediction model using baseline characteristics of tuberculosis (TB) patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring model for predicting the death among newly diagnosed drug sensitive pulmonary TB patients in South India.<h4>Methods</h4>We undertook a longitudinal analysis of cohort data under the Regional Prospec ...[more]

PMID: 36038507

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Project description:BACKGROUND: We conducted a survey to estimate point prevalence of bacteriologically positive pulmonary TB (PTB) in a rural area in South India, implementing TB program DOTS strategy since 2002. METHODS: Survey was conducted among persons ≥ 15 years of age in fifteen clusters selected by simple random sampling; each consisting of 5-12 villages. Persons having symptoms suggestive of PTB or history of anti-TB treatment (ATT) were eligible for sputum examination by smear microscopy for Acid Fast Bacilli and culture for Mycobacterium tuberculosis; two sputum samples were collected from each eligible person. Persons with one or both sputum specimen positive on microscopy and/or culture were labeled suffering from PTB. Prevalence was estimated after imputing missing values to correct for bias introduced by incompleteness of data. In six clusters, registered persons were also screened by X-ray chest. Persons with any abnormal shadow on X-ray were eligible for sputum examination in addition to those with symptoms and ATT. Multiplication factor calculated as ratio of prevalence while using both screening tools to prevalence using symptoms screening alone was applied to entire study population to estimate prevalence corrected for non-screening by X-ray. RESULTS: Of 71,874 residents ≥ 15 years of age, 63,362 (88.2%) were screened for symptoms and ATT. Of them, 5120 (8.1%) - 4681 (7.4%) with symptoms and an additional 439 (0.7%) with ATT were eligible for sputum examination. Spot specimen were collected from 4850 (94.7%) and early morning sputum specimens from 4719 (92.2%). Using symptom screening alone, prevalence of smear, culture and bacteriologically positive PTB in persons ≥ 15 years of age was 83 (CI: 57-109), 152 (CI: 108-197) and 196 (CI :145-246) per 100,000 population respectively. Prevalence corrected for non-screening by X-ray was 108 (CI: 82-134), 198 (CI: 153-243) and 254 (CI: 204-301) respectively. CONCLUSION: Observed prevalence suggests further strengthening of TB control program.

Project description:BackgroundChronic obstructive pulmonary disease (COPD) is a major cause of mortality. Patients with advanced disease often have a poor quality of life, such that guidelines recommend providing palliative care in their last year of life. Uptake and use of palliative care in advanced COPD is low; difficulty in predicting 1-year mortality is thought to be a major contributing factor.MethodsWe identified two primary care COPD cohorts using UK electronic healthcare records (Clinical Practice Research Datalink). The first cohort was randomised equally into training and test sets. An external dataset was drawn from a second cohort. A risk model to predict mortality within 12 months was derived from the training set using backwards elimination Cox regression. The model was given the acronym BARC based on putative prognostic factors including body mass index and blood results (B), age (A), respiratory variables (airflow obstruction, exacerbations, smoking) (R) and comorbidities (C). The BARC index predictive performance was validated in the test set and external dataset by assessing calibration and discrimination. The observed and expected probabilities of death were assessed for increasing quartiles of mortality risk (very low risk, low risk, moderate risk, high risk). The BARC index was compared to the established index scores body mass index, obstructive, dyspnoea and exacerbations (BODEx), dyspnoea, obstruction, smoking and exacerbations (DOSE) and age, dyspnoea and obstruction (ADO).ResultsFifty-four thousand nine hundred ninety patients were eligible from the first cohort and 4931 from the second cohort. Eighteen variables were included in the BARC, including age, airflow obstruction, body mass index, smoking, exacerbations and comorbidities. The risk model had acceptable predictive performance (test set: C-index = 0.79, 95% CI 0.78-0.81, D-statistic = 1.87, 95% CI 1.77-1.96, calibration slope = 0.95, 95% CI 0.9-0.99; external dataset: C-index = 0.67, 95% CI 0.65-0.7, D-statistic = 0.98, 95% CI 0.8-1.2, calibration slope = 0.54, 95% CI 0.45-0.64) and acceptable accuracy predicting the probability of death (probability of death in 1 year, n high-risk group, test set: expected = 0.31, observed = 0.30; external dataset: expected = 0.22, observed = 0.27). The BARC compared favourably to existing index scores that can also be applied without specialist respiratory variables (area under the curve: BARC = 0.78, 95% CI 0.76-0.79; BODEx = 0.48, 95% CI 0.45-0.51; DOSE = 0.60, 95% CI 0.57-0.61; ADO = 0.68, 95% CI 0.66-0.69, external dataset: BARC = 0.70, 95% CI 0.67-0.72; BODEx = 0.41, 95% CI 0.38-0.45; DOSE = 0.52, 95% CI 0.49-0.55; ADO = 0.57, 95% CI 0.54-0.60).ConclusionThe BARC index performed better than existing tools in predicting 1-year mortality. Critically, the risk score only requires routinely collected non-specialist information which, therefore, could help identify patients seen in primary care that may benefit from palliative care.

Dataset Information

Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.

Background

Methods

Results

Conclusions

Publications

Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.

Similar Datasets

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