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  • Springer Science and Business Media LLC  (4)
  • Wang, Zhen  (4)
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  • Springer Science and Business Media LLC  (4)
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  • 1
    Online Resource
    Online Resource
    Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study
    Springer Science and Business Media LLC ; 2023
    In:  Aging Clinical and Experimental Research Vol. 35, No. 3 ( 2023-01-04), p. 639-647
    Details
    In: Aging Clinical and Experimental Research, Springer Science and Business Media LLC, Vol. 35, No. 3 ( 2023-01-04), p. 639-647
    Abstract: Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688–0.768) with the sensitivity of 66.2% (95% CI 58.2–73.6) and specificity of 66.8% (95% CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545–0.737), and sensitivity and specificity were 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681–0.844) with the sensitivity of 63.2% (95% CI 46–78.2) and specificity of 80.5% (95% CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
    Type of Medium: Online Resource
    ISSN: 1720-8319
    URL: Article
    DOI: 10.1007/s40520-022-02325-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2119282-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    The distinct clinical features and prognosis of the CD10+MUM1+ and CD10−Bcl6−MUM1− diffuse large B-cell lymphoma
    Springer Science and Business Media LLC ; 2016
    In:  Scientific Reports Vol. 6, No. 1 ( 2016-02-09)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2016-02-09)
    Abstract: Using an immunohistochemistry (IHC) based method, diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center B-cell (GCB) and non-GCB subtypes. However, the prognostic value of Hans algorithm was contradictory in the literature. Using IHC and fluorescence in situ hybridization, we analyzed the antibodies applied in Hans algorithm and other genetic factors in 601 DLBCL patients and prognostic value of Hans algorithm in 306 cases who were treated with chemoimmunotherapy. The results showed that patients with GCB subtype have better overall survival (OS) and progression-free survival (PFS) than non-GCB cases. However, to some extent, double positive (CD10 + MUM1 + , DP) and triple negative (CD10 − Bcl6 − MUM − , TN) showed different clinical characteristics and prognosis to others that were assigned to the same cell-of-origin group. The DP group showed similar OS (median OS: both not reached, P  = 0.3650) and PFS (median PFS: 47.0 vs. 32.7 months, P  = 0.0878) with the non-GCB group while the TN group showed similar OS (median OS: both not reached, P  = 0.9278) and PFS (median PFS: both not reached, P  = 0.9420) with the GCB group. In conclusion, Recognition of specific entities in Hans algorithm could help us to accurately predict outcome of the patients and choose the best clinical management for them.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/srep20465
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2615211-3
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  • 3
    Online Resource
    Online Resource
    Epstein-Barr virus positive diffuse large B-cell lymphoma predict poor outcome, regardless of the age
    Springer Science and Business Media LLC ; 2015
    In:  Scientific Reports Vol. 5, No. 1 ( 2015-07-23)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2015-07-23)
    Abstract: Epstein-Barr virus (EBV) positive diffuse large B-cell lymphoma (DLBCL) of the elderly is defined as patients older than 50 years alone. However, recent studies showed young patients with sound immune status could also be affected. In this study, we investigated the clinical features and outcomes of patients with EBV positive DLBCL in the different age groups using different EBER cut-off values. The prevalence of EBV positive DLBCL was 14.0% (35/250) and 10.4% (26/250) for EBER cut-off of 20% and 50%, respectively. With both EBER cut-off values, patients with EBV DLBCL shared many unfavorable prognostic characteristics, regardless of age. EBV positive patients, both in the elderly and young groups, showed significantly worse overall survival and progression-free survival than negative cases. Moreover, no significant differences of outcomes were identified between different age groups with EBV positive DLBCL. In conclusion, EBV positive DLBCL patients, regardless of age, shared similar poor prognostic features and showed worse outcome than negative cases. We suggest that the age criterion of EBV positive DLBCL of the elderly and possibly the name itself, be modified in future.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/srep12168
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2015
    detail.hit.zdb_id: 2615211-3
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  • 4
    Online Resource
    Online Resource
    Donor-derived CD19 CAR-T cell therapy of relapse of CD19-positive B-ALL post allotransplant
    Springer Science and Business Media LLC ; 2021
    In:  Leukemia Vol. 35, No. 6 ( 2021-06), p. 1563-1570
    Details
    In: Leukemia, Springer Science and Business Media LLC, Vol. 35, No. 6 ( 2021-06), p. 1563-1570
    Abstract: Safety and efficacy of allogeneic anti-CD19 chimeric antigen receptor T cells (CAR-T cells) in persons with CD19-positive B-cell acute lymphoblastic leukemia (B-ALL) relapsing after an allotransplant remain unclear. Forty-three subjects with B-ALL relapsing post allotransplant received CAR-T cells were analyzed. 34 (79%; 95% confidence interval [CI]: 66, 92%) achieved complete histological remission (CR). Cytokine release syndrome (CRS) occurred in 38 (88%; 78, 98%) and was ≥grade-3 in 7. Two subjects died from multiorgan failure and CRS. Nine subjects (21%; 8, 34%) developed ≤grade-2 immune effector cell-associated neurotoxicity syndrome (ICANS). Two subjects developed ≤grade-2 acute graft- versus -host disease (G v HD). 1-year event-free survival (EFS) and survival was 43% (25, 62%). In 32 subjects with a complete histological remission without a second transplant, 1-year cumulative incidence of relapse was 41% (25, 62%) and 1-year EFS and survival, 59% (37, 81%). Therapy of B-ALL subjects relapsing post transplant with donor-derived CAR-T cells is safe and effective but associated with a high rate of CRS. Outcomes seem comparable to those achieved with alternative therapies but data from a randomized trial are lacking.
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    URL: Article
    DOI: 10.1038/s41375-020-01056-6
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2008023-2
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