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Development and validation of a machine learning model to predict prognosis in HIV-negative cryptococcal meningitis patients: a multicenter study

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European Journal of Clinical Microbiology & Infectious Diseases Aims and scope Submit manuscript

Abstract

Purpose

To predict prognosis in HIV-negative cryptococcal meningitis (CM) patients by developing and validating a machine learning (ML) model.

Methods

This study involved 523 HIV-negative CM patients diagnosed between January 1, 1998, and August 31, 2022, by neurologists from 3 tertiary Chinese centers. Prognosis was evaluated at 10 weeks after the initiation of antifungal therapy.

Results

The final prediction model for HIV-negative CM patients comprised 8 variables: Cerebrospinal fluid (CSF) cryptococcal count, CSF white blood cell (WBC), altered mental status, hearing impairment, CSF chloride levels, CSF opening pressure (OP), aspartate aminotransferase levels at admission, and decreased rate of CSF cryptococcal count within 2 weeks after admission. The areas under the curve (AUCs) in the internal, temporal, and external validation sets were 0.87 (95% CI 0.794–0.944), 0.92 (95% CI 0.795–1.000), and 0.86 (95% CI 0.744–0.975), respectively. An artificial intelligence (AI) model was trained to detect and count cryptococci, and the mean average precision (mAP) was 0.993.

Conclusion

A ML model for predicting prognosis in HIV-negative CM patients was built and validated, and the model might provide a reference for personalized treatment of HIV-negative CM patients. The change in the CSF cryptococcal count in the early phase of HIV-negative CM treatment can reflect the prognosis of the disease. In addition, utilizing AI to detect and count CSF cryptococci in HIV-negative CM patients can eliminate the interference of human factors in detecting cryptococci in CSF samples and reduce the workload of the examiner.

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Data availability

The datasets used and analyzed in this study are available from the corresponding author on reasonable request.

Abbreviations

CM :

Cryptococcal meningitis

CSF :

Cerebrospinal fluid

WBC :

White blood cell

ML :

Machine learning

AI :

Artificial intelligence

TRIPOD :

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis

OP :

Opening pressure

BMI :

Body mass index

AUC :

Area under the curve

MAP :

Mean average precision

IOU :

Intersection over union

PR :

Precision recall

LR :

Logistic regression

WSI :

Whole-slide images

IQR :

Interquartile range

LASSO :

Least absolute shrinkage and the selection operator

RF :

Random forest

KNN :

k-Nearest neighbours

DT :

Decision tree

EFA :

Early fungicidal activity

CFU :

Colony-forming-units

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Acknowledgements

The authors thank Jianmin Li and Weiwei Gao of Unicom (Guangdong) Industrial internet Co., Ltd. for their contribution to the webpage construction of the prediction model and AI-assisted cryptococcal counting in this study.

Funding

The study was supported by the National Science Foundation of China (No. 82071265).

Author information

Author notes

  1. Junyu Liu, Yaxin Lu, Jia Liu, and Jiayin Liang contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China

    Junyu Liu, Jia Liu, Liuxu Fan, Lu Yang, Xiaohong Su, Anni Wang, Lei Xiong, Ying Jiang & Fuhua Peng

  2. Big Data and Artificial Intelligence Center, The Third Hospital of Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China

    Yaxin Lu & Zifeng Liu

  3. Department of Laboratory, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China

    Jiayin Liang & Panpan Liang

  4. Department of Neurology, Jiangxi Chest Hospital, Jiangxi, 330000, China

    Qilong Zhang, Jia Xie & Haiying Chen

  5. Department of Neurology, Cangshan Breach of the 900Th Hospital of PLA Joint Service Support Force, Fuzhou, 350000, Fujian, China

    Hua Li

  6. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Sun Yat-Sen University, Guangzhou, China

    Xiufeng Zhong & Yuan Wang

  7. Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China

    Hui Bu

  8. Department of Neurology, Guangdong 999 Brain Hospital, Guangzhou, China

    Zhanhang Wang & Yangyang Dai

  9. Department of Neurology, Fujian Medical University Union Hospital, Xinquan Road 29#, Fuzhou, 350001, China

    Jiayin Gong

  10. Department of Scientific Affairs, Hugobiotech Co., Ltd, Beijing, China

    Han Xia

Authors
  1. Junyu Liu
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  2. Yaxin Lu
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Contributions

Junyu Liu, Yaxin Lu, Ying Jiang, Zifeng Liu, and Fuhua Peng contributed to the study design. Fuhua Peng, Ying Jiang, Junyu Liu, Jia Liu, and Jiayin Liang did the literature search. Yaxin Lu and Zifeng Liu analyzed the data and created figures and tables. Junyu Liu wrote the first draft. Junyu Liu, Qilong Zhang, Hua Li, Xiufeng Zhong, Hui Bu, Zhanhang Wang, Liuxu Fan, Panpan Liang, Jia Xie, Yuan Wang, Jiayin Gong, Haiying Chen, Yangyang Dai, Lu Yang, Xiaohong Su, Anni Wang, Lei Xiong, and Han Xia contributed to the data collection. All authors contributed to the interpretation of results, reviewed and critically revised the manuscript, and approved the final version for submission.

Corresponding authors

Correspondence to Ying Jiang, Zifeng Liu or Fuhua Peng.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving the patient included were in accordance with the ethical standards of the institutional board and with the 1964 Helsinki Declaration. This study is approved by the Medical Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (approval no. [2022] 02-166-01). At admission, the subjects or the guardians of patients with cognitive impairment provided written informed consent for research and publication.

Consent for publication

We have obtained consent to publish from the participant to report individual patient data.

Conflict of interest

The authors declare that they have no competing interests.

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Supplementary Information

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Supplementary file1 (DOCX 20 KB)

Supplementary Fig. 1

Correlation coefficient chart between variables. (PNG 555 kb)

High resolution image (TIF 3899 kb)

Supplementary Fig. 2

Feature selection using least absolute shrinkage and the selection operator (LASSO) regression model. (PNG 178 kb)

High resolution image (TIF 407 kb)

Supplementary Fig. 3

Calibration curve: (A)internal validation set, (B)temporal validation set, (C)external validation set. (PNG 339 kb)

High resolution image (TIF 2275 kb)

Supplementary Fig. 4

Online webpage: (A) cryptococcal automatic count, (B) Calculator for risk prediction model. (PNG 1528 kb)

High resolution image (TIF 6679 kb)

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Liu, J., Lu, Y., Liu, J. et al. Development and validation of a machine learning model to predict prognosis in HIV-negative cryptococcal meningitis patients: a multicenter study. Eur J Clin Microbiol Infect Dis 42, 1183–1194 (2023). https://doi.org/10.1007/s10096-023-04653-2

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