Trial reportInternal and emergency medicine2023
A machine learning diagnostic model for Pneumocystis jirovecii pneumonia in patients with severe pneumonia.
Trial report in Internal and emergency medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters.Scientific reports · 2026Article
- Development and Validation of a Machine Learning-Based Nomogram for Predicting Severe Pneumocystis jirovecii Pneumonia in Kidney Transplant Recipients.Annals of transplantation · 2026Article
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Risk prediction of pneumocystis jirovecii pneumonia in kidney transplant recipients using a clinical nomogram.Frontiers in cellular and infection microbiology · 2026Article
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Development and validation of supervised machine learning multivariable prediction models for the diagnosis of Pneumocystis jirovecii pneumonia using nasopharyngeal swab PCR in adults in a low-HIV prevalence setting.International health · 2025Article
- A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.Journal of cellular and molecular medicine · 2025Article
- Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.The clinical respiratory journal · 2024Article
- [Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Candent issues in pneumonia. Reflections from the Fifth Annual Meeting of Spanish Experts 2023.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe diagnosis of Pneumocystis jirovecii pneumonia (PCP) in patients presenting with severe pneumonia is challenging and delays in treatment were associated with worse prognosis. This study aimed to develop a rapid, easily available, noninvasive machine learning diagnostic model for PCP among patients with severe pneumonia.
methodsA retrospective study was performed in West China Hospital among consecutive patients with severe pneumonia who had undergone bronchoalveolar lavage for etiological evaluation between October 2010 and April 2021. Factors associated with PCP were identified and four diagnostic models were established using machine learning algorithms including Logistic Regression, eXtreme Gradient Boosting, Random Forest (RF) and LightGBM. The performance of these models were evaluated by the area under the receiver operating characteristic curve (AUC).
resultsUltimately, 704 patients were enrolled and randomly divided into a training set (n = 564) and a testing set (n = 140). Four factors were ultimately selected to establish the model including neutrophil, globulin, β-D-glucan and ground glass opacity. The RF model exhibited the greatest diagnostic performance with an AUC of 0.907. The calibration curve and decision curve analysis also demonstrated its accuracy and applicability.
conclusionsWe constructed a PCP diagnostic model in patients with severe pneumonia using four easily available and noninvasive clinical indicators. With satisfying diagnostic performance and good clinical practicability, this model may help clinicians to make early diagnosis of PCP, reduce the delays of treatment and improve the prognosis among these patients.
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Registered trials
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 Socratic graph.