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The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation
Sheryl Hui-Xian Ng1, Zi Yan Chiam2, Gin Tsen Chai3,4
1Health Services and Outcomes Research, National Healthcare Group, Annex @ National Skin Centre, 1 Mandalay Road, Singapore, 308205, Singapore. Sheryl_hx_ng@nhg.com.sg.
BMC Pulmonary Medicine
|August 30, 2024
Summary
A new prognostic model predicts one-year mortality in patients with chronic lung diseases (CLDs). This tool aids early palliative care referrals for patients at high risk of deterioration.
Area of Science:
- Pulmonary Medicine
- Geriatrics
- Palliative Care
Background:
- Chronic lung diseases (CLDs) present a significant symptom burden in advanced stages.
- Palliative care referrals for CLD patients are often delayed.
- Existing prognostic tools may lack generalizability across diverse CLD types.
Purpose of the Study:
- To develop and validate a generalized prognostic model for predicting one-year mortality in patients with any CLD.
- To improve early identification of CLD patients requiring palliative care assessment.
Main Methods:
- Retrospective cohort study utilizing electronic medical records from July 2016 to October 2017.
- Development and validation of a logistic regression model to predict all-cause mortality within one year of diagnosis.
- Multiple imputation for missing data and assessment of model discriminative ability, calibration, and clinical usefulness.
Main Results:
- The final model, selected via forward stepwise variable selection, included ten prognostic factors.
- The model demonstrated good discriminative ability with an AUC of 0.77 (0.72-0.82) in the development cohort and 0.75 (0.70-0.81) in the validation cohort.
- The model accurately identified 30% of decedents and 90% of survivors when classifying patients with a predicted risk of death exceeding 0.30 as high risk.
Conclusions:
- A validated prognostic model for one-year mortality in CLD patients was developed using administrative data.
- This model supports clinicians in identifying at-risk patients across various CLD etiologies.
- Early identification facilitates palliative care assessment and timely referral, addressing unmet patient needs.
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