The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation

Background: Patients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patient...

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Main Authors: Ng, Sheryl Hui-Xian, Chiam, Zi Yan, Chai, Gin Tsen, Kaur, Palvinder, Yip, Wan Fen, Low, Zhi Jun, Chu, Jermain, Tey, Lee Hung, Neo, Han Yee, Tan, Woan Shin, Hum, Allyn
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/181320
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-181320
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Medicine, Health and Life Sciences
Bronchiectasis
End-of-life
spellingShingle Medicine, Health and Life Sciences
Bronchiectasis
End-of-life
Ng, Sheryl Hui-Xian
Chiam, Zi Yan
Chai, Gin Tsen
Kaur, Palvinder
Yip, Wan Fen
Low, Zhi Jun
Chu, Jermain
Tey, Lee Hung
Neo, Han Yee
Tan, Woan Shin
Hum, Allyn
The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
description Background: Patients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patients at high risk of deterioration for needs assessments and referral to palliative care. As current prognostic tools may not generalize well across all CLD conditions, we aim to develop and validate a general model to predict one-year mortality in patients presenting with any CLD. Methods: A retrospective cohort study of patients with a CLD diagnosis at a public hospital from July 2016 to October 2017 was conducted. The outcome of interest was all-cause mortality within one-year of diagnosis. Potential prognostic factors were identified from reviews of prognostic studies in CLD, and data was extracted from electronic medical records. Missing data was imputed using multiple imputation by chained equations. Logistic regression models were developed using variable selection methods and validated in patients seen from January 2018 to December 2019. Discriminative ability, calibration and clinical usefulness of the model was assessed. Model coefficients and performance were pooled across all imputed datasets and reported. Results: Of the 1000 patients, 122 (12.2%) died within one year. Patients had chronic obstructive pulmonary disease or emphysema (55%), bronchiectasis (38%), interstitial lung diseases (12%), or multiple diagnoses (6%). The model selected through forward stepwise variable selection had the highest AUC (0.77 (0.72–0.82)) and consisted of ten prognostic factors. The model AUC for the validation cohort was 0.75 (0.70, 0.81), and the calibration intercept and slope were − 0.14 (-0.54, 0.26) and 0.74 (0.53, 0.95) respectively. Classifying patients with a predicted risk of death exceeding 0.30 as high risk, the model would correctly identify 3 out 10 decedents and 9 of 10 survivors. Conclusions: We developed and validated a prognostic model for one-year mortality in patients with CLD using routinely available administrative data. The model will support clinicians in identifying patients across various CLD etiologies who are at risk of deterioration for a basic palliative care assessment to identify unmet needs and trigger an early referral to palliative medicine. Trial registration: Not applicable (retrospective study).
author2 Lee Kong Chian School of Medicine (LKCMedicine)
author_facet Lee Kong Chian School of Medicine (LKCMedicine)
Ng, Sheryl Hui-Xian
Chiam, Zi Yan
Chai, Gin Tsen
Kaur, Palvinder
Yip, Wan Fen
Low, Zhi Jun
Chu, Jermain
Tey, Lee Hung
Neo, Han Yee
Tan, Woan Shin
Hum, Allyn
format Article
author Ng, Sheryl Hui-Xian
Chiam, Zi Yan
Chai, Gin Tsen
Kaur, Palvinder
Yip, Wan Fen
Low, Zhi Jun
Chu, Jermain
Tey, Lee Hung
Neo, Han Yee
Tan, Woan Shin
Hum, Allyn
author_sort Ng, Sheryl Hui-Xian
title The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
title_short The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
title_full The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
title_fullStr The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
title_full_unstemmed The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
title_sort prognostic model for chronic lung disease (pro-mel): development and temporal validation
publishDate 2024
url https://hdl.handle.net/10356/181320
_version_ 1816858942552145920
spelling sg-ntu-dr.10356-1813202024-11-25T06:54:07Z The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation Ng, Sheryl Hui-Xian Chiam, Zi Yan Chai, Gin Tsen Kaur, Palvinder Yip, Wan Fen Low, Zhi Jun Chu, Jermain Tey, Lee Hung Neo, Han Yee Tan, Woan Shin Hum, Allyn Lee Kong Chian School of Medicine (LKCMedicine) Tan Tock Seng Hospital Medicine, Health and Life Sciences Bronchiectasis End-of-life Background: Patients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patients at high risk of deterioration for needs assessments and referral to palliative care. As current prognostic tools may not generalize well across all CLD conditions, we aim to develop and validate a general model to predict one-year mortality in patients presenting with any CLD. Methods: A retrospective cohort study of patients with a CLD diagnosis at a public hospital from July 2016 to October 2017 was conducted. The outcome of interest was all-cause mortality within one-year of diagnosis. Potential prognostic factors were identified from reviews of prognostic studies in CLD, and data was extracted from electronic medical records. Missing data was imputed using multiple imputation by chained equations. Logistic regression models were developed using variable selection methods and validated in patients seen from January 2018 to December 2019. Discriminative ability, calibration and clinical usefulness of the model was assessed. Model coefficients and performance were pooled across all imputed datasets and reported. Results: Of the 1000 patients, 122 (12.2%) died within one year. Patients had chronic obstructive pulmonary disease or emphysema (55%), bronchiectasis (38%), interstitial lung diseases (12%), or multiple diagnoses (6%). The model selected through forward stepwise variable selection had the highest AUC (0.77 (0.72–0.82)) and consisted of ten prognostic factors. The model AUC for the validation cohort was 0.75 (0.70, 0.81), and the calibration intercept and slope were − 0.14 (-0.54, 0.26) and 0.74 (0.53, 0.95) respectively. Classifying patients with a predicted risk of death exceeding 0.30 as high risk, the model would correctly identify 3 out 10 decedents and 9 of 10 survivors. Conclusions: We developed and validated a prognostic model for one-year mortality in patients with CLD using routinely available administrative data. The model will support clinicians in identifying patients across various CLD etiologies who are at risk of deterioration for a basic palliative care assessment to identify unmet needs and trigger an early referral to palliative medicine. Trial registration: Not applicable (retrospective study). National Medical Research Council (NMRC) Published version This work was supported by the National Medical Research Council, Singapore [grant number HSRGEoL18may-0003]. The funder had no involvement in any aspect of this study or decision to publish. 2024-11-25T06:54:07Z 2024-11-25T06:54:07Z 2024 Journal Article Ng, S. H., Chiam, Z. Y., Chai, G. T., Kaur, P., Yip, W. F., Low, Z. J., Chu, J., Tey, L. H., Neo, H. Y., Tan, W. S. & Hum, A. (2024). The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation. BMC Pulmonary Medicine, 24(1), 429-. https://dx.doi.org/10.1186/s12890-024-03233-0 1471-2466 https://hdl.handle.net/10356/181320 10.1186/s12890-024-03233-0 39215286 2-s2.0-85202748552 1 24 429 en HSRGEoL18may-0003 BMC Pulmonary Medicine © 2024 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. application/pdf