Evidence map›Paper›PMID 36038507›Full record

Observational studyJournal of public health (Oxford, England)2023

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

Yuvaraj Krishnamoorthy, Komala Ezhumalai, Sharan Murali, Sathish Rajaa, Marie Gilbert Majella, Sonali Sarkar, Subitha Lakshminarayanan, Noyal Mariya Joseph, Govindarajan Soundappan, Senbagavalli Prakash Babu and 8 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Journal of public health (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Yuvaraj KrishnamoorthyDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.ORCID 0000-0003-4688-510X
Komala EzhumalaiDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Sharan MuraliDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Sathish RajaaDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Marie Gilbert MajellaDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Sonali SarkarDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Subitha LakshminarayananDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Noyal Mariya JosephDepartment of Microbiology, JIPMER, Puducherry 605 006, India.
Govindarajan SoundappanState TB Cell, Directorate of Health Services, Puducherry 605001, India.
Senbagavalli Prakash BabuDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Charles HorsburghDepartment of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA.
Natasha HochbergDepartment of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118, USA.
W Evan JohnsonDepartment of Medicine and Biostatistics, Boston University School of Medicine, Boston, MA 02118, USA.
Selby KnudsenDepartment of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118, USA.
Sri Ram PentakotaDepartment of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey 07103, USA.
Padmini SalgameDepartment of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey 07103, USA.
Gautam RoyDepartment of Preventive & Social Medicine, JIPMER, Puducherry 605 006, India.
Jerrold EllnerDepartment of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey 07103, USA.

Funding

NIH HHS
6 · The paper itself

Abstract

backgroundDevelopment 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.

methodsWe 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.

resultsIn 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%.

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

Indexed as

TuberculosisTuberculosis, PulmonaryHumansIndiaNomogramsProbabilityPrognosisRetrospective StudiesIndiamortalitynomogramsprognosistuberculosis

Identifiers

PMID36038507
PMCPMC10273380

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.