Browsing by Subject "Proportional Hazards Models"

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    Comparison of matrix frequency-doubling technology perimetry and standard automated perimetry in monitoring the development of visual field defects for glaucoma suspect eyes
    (PLOS, 2017-05-18) Hu, Rongrong; Wang, Chenkun; Racette, Lyne; Ophthalmology, School of Medicine
    BACKGROUND: Perimetry is indispensable for the clinical management of glaucoma suspects. Our goal is to compare the performance of standard automated perimetry (SAP) and Matrix frequency-doubling technology (FDT) perimetry in monitoring the development of visual field (VF) defects in glaucoma suspect eyes. METHODS: Longitudinal data of paired SAP and FDT from 221 eyes of 155 glaucoma suspects enrolled in the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. All eyes had glaucomatous optic neuropathy or ocular hypertension, but normal SAP and FDT results at baseline. The development of glaucomatous VF defects was defined as the presence of a cluster of ≥ 3 (less conservative) or ≥ 4 (more conservative) locations confirmed on ≥ 2 additional consecutive tests. Risk factors for the development of VF defects were analyzed by COX proportional hazard models. After conversion into common logarithmic units, the rates of change of global VF indices were fitted with linear mixed models. RESULTS: FDT detected more eyes that developed VF defects than SAP using the less conservative criterion, and no significant difference was observed using the more conservative criterion. For those eyes detected by both SAP and FDT, FDT detected the development of VF defects either earlier than SAP or simultaneously in most cases. Baseline structural measurements were not significantly associated with an increased risk for the development of glaucomatous VF defects on either SAP or FDT. Older age was significantly associated with the development of VF defects on FDT but not on SAP. Both SAP and FDT detected a progressing worsening trend of pattern standard deviation over time with a similar rate of change between these test types. CONCLUSIONS: Matrix FDT would be useful to monitor the onset of VF defects in glaucoma suspects and may outperform SAP in the early stage of glaucomatous VF damage.
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    Survival Analysis of Endodontically Treated Teeth in Patients with Diabetes and Hypertension within National Dental PBRN Practices
    (2022-06) Crosby, William Justin; Spolnik, Kenneth; Thyvalikakath, Thankam Paul; Ehrlich, Ygal; Warner, Ned
    Introduction: The prevalence of diabetes mellitus (DM) is rapidly increasing among the aging United States population. This poses a challenge to dental providers since DM and multiple oral conditions have been identified as comorbidities. Hypertension (HTN) is associated with more poorly controlled DM and has been identified as contributing to RCT tooth loss in prior studies. Links have also been established between DM and the survival rate of root canal treated teeth, however, previous research has focused on institutional settings despite the majority of RCT being performed in private dental practices. This study will use data from private dental practices to evaluate the survival rate of RCT teeth in patients with DM and HTN. Materials and Methods: This retrospective study evaluated the survival rate of endodontic treated teeth among patients with DM and HTN using National Dental PBRN Practice data. Electronic dental records from 42 private dental practices in the United States over a period of 15 years with a minimum 2-year follow-up comprising 11,532 root canal treated teeth were analyzed. Kaplan-Meier survival curves were used to demonstrate the effects of HTN and DM on RCT tooth survival and Cox proportional hazards survival analysis was used to evaluate the DM and HTN effects after accounting for age, gender, insurance, year of treatment, tooth type, and crown and filling placement as covariates. Results: Patients with HTN only had significantly lower risk of failure than patients with both HTN and DM (p=0.003). Patients with neither HTN nor DM had significantly lower risk of failure than patients with both HTN and DM (p=0.020). Patients with DM only did not have significantly different risk of failure than patients with both HTN and DM (p=0.223). Patients with DM only did not have significantly different risk of failure than patients with HTN only (p=0.361). Patients with neither HTN nor DM did not have significantly different risk of failure than patients with HTN only (p=0.121) or patients with DM only (p=0.800). Conclusions: Patients with both DM and HTN have an increased chance of root canal treated tooth failure while patients with only DM or only HTN do not. Evaluation of severity of DM may be more important in determining RCT failure and studies utilizing laboratory values should be considered for future research.
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    Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images
    (IEEE Xplore, 2021-12) Shao, Wei; Wang, Tongxin; Huang, Zhi; Han, Zhi; Zhang, Jie; Huang, Kun; Medical and Molecular Genetics, School of Medicine
    Whole-Slide Histopathology Image (WSI) is generally considered the gold standard for cancer diagnosis and prognosis. Given the large inter-operator variation among pathologists, there is an imperative need to develop machine learning models based on WSIs for consistently predicting patient prognosis. The existing WSI-based prediction methods do not utilize the ordinal ranking loss to train the prognosis model, and thus cannot model the strong ordinal information among different patients in an efficient way. Another challenge is that a WSI is of large size (e.g., 100,000-by-100,000 pixels) with heterogeneous patterns but often only annotated with a single WSI-level label, which further complicates the training process. To address these challenges, we consider the ordinal characteristic of the survival process by adding a ranking-based regularization term on the Cox model and propose a weakly supervised deep ordinal Cox model (BDOCOX) for survival prediction from WSIs. Here, we generate amounts of bags from WSIs, and each bag is comprised of the image patches representing the heterogeneous patterns of WSIs, which is assumed to match the WSI-level labels for training the proposed model. The effectiveness of the proposed method is well validated by theoretical analysis as well as the prognosis and patient stratification results on three cancer datasets from The Cancer Genome Atlas (TCGA).