Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study
Abstract Background Invasive fungal disease (IFD) is characterized by its capacity to rapidly escalate to life-threatening conditions, even when patients are hospitalized. However, the precise prognostic significance of baseline clinical characteristics related to the progression outcome of IFD rema...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-025-11030-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849728333006241792 |
|---|---|
| author | Wei Wang Yan Li Hua Wang Yumeng Du Mengyuan Cheng Jinyan Tang Mingliang Wu Chaomin Chen Qingwen Lv Weibin Cheng |
| author_facet | Wei Wang Yan Li Hua Wang Yumeng Du Mengyuan Cheng Jinyan Tang Mingliang Wu Chaomin Chen Qingwen Lv Weibin Cheng |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Abstract Background Invasive fungal disease (IFD) is characterized by its capacity to rapidly escalate to life-threatening conditions, even when patients are hospitalized. However, the precise prognostic significance of baseline clinical characteristics related to the progression outcome of IFD remains elusive. Methods A retrospective cohort study spanning a duration of 10 years was conducted at two prominent tertiary teaching hospitals in Southern China. Patients with proven IFD were queried and divided into serious and non-serious groups based on the disease deterioration. To establish robust predictive models, patients from the first hospital were randomly assigned to either a training set or an internal validation set, while patients from the second hospital constituted an external test set. To analyze the potential predictors of IFD deterioration and identify independent predictors, the study employed the least absolute shrinkage and selection operator (LASSO) method in conjunction with binary logistic regressions. Based on the outcomes of this analysis, a predictive nomogram was constructed. The performance of the developed model was thoroughly evaluated using the training set, internal validation set, and external test set. Results A total of 480 cases from the first hospital and 256 cases from the second hospital were included in the study. Among the 480 patients, 81 cases (16.9%) experienced deterioration, and out of those, 45 (55.6%) cases resulted in mortality. Seven independent predictors were identified and utilized to construct a predictive nomogram. The nomogram exhibited excellent predictive performance in all three sets: the training set, internal validation set, and external test set. The area under the receiver operating characteristic curve (AUC) for the training set was 0.88, for the internal validation set was 0.91, and for the external test set was 0.90. The Hosmer–Lemeshow test and Brier score indicated a high goodness of fit for the model. Furthermore, the calibration curve demonstrated a strong agreement between the predicted outcomes from the nomogram and the actual observations. Additionally, the decision curve analysis exhibited that the nomogram provided significant clinical net benefits in predicting IFD deterioration. Conclusions The study successfully identified seven independent predictors and developed a predictive nomogram for early assessment of the likelihood of IFD deterioration. |
| format | Article |
| id | doaj-art-6708fc254d8440feaa1ad0847e4d4536 |
| institution | DOAJ |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-6708fc254d8440feaa1ad0847e4d45362025-08-20T03:09:35ZengBMCBMC Infectious Diseases1471-23342025-05-0125111410.1186/s12879-025-11030-1Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort studyWei Wang0Yan Li1Hua Wang2Yumeng Du3Mengyuan Cheng4Jinyan Tang5Mingliang Wu6Chaomin Chen7Qingwen Lv8Weibin Cheng9Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan UniversityDepartment of General Medicine, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityGeneral Practice Medicine, Zhujiang Hospital, Southern Medical UniversityInstitute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan UniversityInstitute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan UniversityGeneral Practice Medicine, Zhujiang Hospital, Southern Medical UniversityGeneral Practice Medicine, Zhujiang Hospital, Southern Medical UniversitySchool of Biomedical Engineering, Southern Medical UniversityDepartment of Information, Zhujiang Hospital, Southern Medical UniversityInstitute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan UniversityAbstract Background Invasive fungal disease (IFD) is characterized by its capacity to rapidly escalate to life-threatening conditions, even when patients are hospitalized. However, the precise prognostic significance of baseline clinical characteristics related to the progression outcome of IFD remains elusive. Methods A retrospective cohort study spanning a duration of 10 years was conducted at two prominent tertiary teaching hospitals in Southern China. Patients with proven IFD were queried and divided into serious and non-serious groups based on the disease deterioration. To establish robust predictive models, patients from the first hospital were randomly assigned to either a training set or an internal validation set, while patients from the second hospital constituted an external test set. To analyze the potential predictors of IFD deterioration and identify independent predictors, the study employed the least absolute shrinkage and selection operator (LASSO) method in conjunction with binary logistic regressions. Based on the outcomes of this analysis, a predictive nomogram was constructed. The performance of the developed model was thoroughly evaluated using the training set, internal validation set, and external test set. Results A total of 480 cases from the first hospital and 256 cases from the second hospital were included in the study. Among the 480 patients, 81 cases (16.9%) experienced deterioration, and out of those, 45 (55.6%) cases resulted in mortality. Seven independent predictors were identified and utilized to construct a predictive nomogram. The nomogram exhibited excellent predictive performance in all three sets: the training set, internal validation set, and external test set. The area under the receiver operating characteristic curve (AUC) for the training set was 0.88, for the internal validation set was 0.91, and for the external test set was 0.90. The Hosmer–Lemeshow test and Brier score indicated a high goodness of fit for the model. Furthermore, the calibration curve demonstrated a strong agreement between the predicted outcomes from the nomogram and the actual observations. Additionally, the decision curve analysis exhibited that the nomogram provided significant clinical net benefits in predicting IFD deterioration. Conclusions The study successfully identified seven independent predictors and developed a predictive nomogram for early assessment of the likelihood of IFD deterioration.https://doi.org/10.1186/s12879-025-11030-1Invasive fungal diseaseIndependent predictorNomogramPrediction probability |
| spellingShingle | Wei Wang Yan Li Hua Wang Yumeng Du Mengyuan Cheng Jinyan Tang Mingliang Wu Chaomin Chen Qingwen Lv Weibin Cheng Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study BMC Infectious Diseases Invasive fungal disease Independent predictor Nomogram Prediction probability |
| title | Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study |
| title_full | Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study |
| title_fullStr | Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study |
| title_full_unstemmed | Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study |
| title_short | Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study |
| title_sort | predictive nomogram for early detection of invasive fungal disease deterioration a 10 year retrospective cohort study |
| topic | Invasive fungal disease Independent predictor Nomogram Prediction probability |
| url | https://doi.org/10.1186/s12879-025-11030-1 |
| work_keys_str_mv | AT weiwang predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT yanli predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT huawang predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT yumengdu predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT mengyuancheng predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT jinyantang predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT mingliangwu predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT chaominchen predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT qingwenlv predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy AT weibincheng predictivenomogramforearlydetectionofinvasivefungaldiseasedeteriorationa10yearretrospectivecohortstudy |