Screening Algorithm for BK Virus-Associated Nephropathy Using Sequential Testing of Urinary Cytology: A Probabilistic Model Analysis

K.M., Maggie; Leung, Anskar Y. H.; Lo, Kin Yee; Lio, Weng In; Chan, Hoi Wong; Wong, O. I.; Hau, Anthony K.C.; Tam, Chun Hay; Wong, Andrew K.M.; Kuok, Un I; Chau, Kwong Wing; Fung, Samuel; Kwan, Tsz Hoi; Wong, S.P.; Tang, Sydney C.W.

doi:10.1159/000443514

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Timely decrease of IS is the only measure that consistently impacts the course of PyVAN . Screening protocols to identify patients at risk of PyVAN are based on identification of “decoy cells” in urine cytology and viral loads in urine and blood . Once the diagnosis of PyVAN is made, BK viremia is the tool that most dependably reflects the burden of parenchymal disease .…”

Section: Discussionmentioning

confidence: 99%

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Histological Evolution of BK Virus–Associated Nephropathy: Importance of Integrating Clinical and Pathological Findings

Drachenberg

Chaudhry

Ugarte

et al. 2017

American Journal of Transplantation

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Long-term clinicopathological studies of BK-associated nephropathy (PyVAN) are not available. We studied 206 biopsies (71 patients), followed 3.09 ± 1.46 years after immunosuppression reduction. The biopsy features (% immunostain for PyV large T ag + staining and inflammation ± acute rejection) were correlated with viral load dynamics and serum creatinine to define the clinicopathological status (PyVCPS). Incidence of acute rejection was 28% in the second biopsy and 50% subsequently (25% mixed T cell-mediated allograft rejection (TCMR) + antibody-mediated allograft rejection (AMR); rejection overall affected 38% of patients (>50% AMR). Graft loss was 15.4% (0.8-5.3 years after PyVAN); 76% had complete viral clearance (mean 28 weeks). The only predictors of graft loss were acute rejection (TCMR p = 0.008, any type p = 0.07), and increased "t" and "ci" in the second biopsy (p = 0.006 and 0.048). Higher peak viremia correlated with poorer viral clearance (p = 0.002). Presumptive and proven PyVAN had similar presentation, evolution, and outcome. Late PyVAN (>2 years, 9.8%) justifies BK viremia evaluation at any point with graft dysfunction and/or biopsy evaluation. This study describes the histological evolution of PyVAN and corresponding clinicopathological correlations. Although the pathological features overall reflect the viral and immunological interactions, the PyVAN course remains difficult to predict based on any single feature. Appropriate clinical management requires repeat biopsies and determination of the PyVCPS at relevant time points, for corresponding personalized immunosuppression adjustment.

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Section: Discussionmentioning

confidence: 99%

“…Screening protocols for early diagnosis of polyomavirus BK–associated nephropathy (PyVAN) resulted in outcome improvements, particularly in the short term . Unfortunately, sclerosing sequelae and increased risk of rejection contribute to poorer outcomes in the long term .…”

Section: Introductionmentioning

confidence: 99%

Histological Evolution of BK Virus–Associated Nephropathy: Importance of Integrating Clinical and Pathological Findings

Drachenberg

Chaudhry

Ugarte

et al. 2017

American Journal of Transplantation

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“…To improve the limited positive predictive values (PPVs) delivered by psychopathology-based classifications associated with CHR instruments, 9 models with biological, neurocognitive or environmental data have been developed. In fact, the use of predictive models 10 along with sequential multistage testing 11 is common practice in preventive medicine to improve prognostic discrimination between individuals who will develop a certain condition and those who will not.…”

Section: Introductionmentioning

confidence: 99%

Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Schmidt

Cappucciati

Raduà

et al. 2016

SCHBUL

117

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Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%–82% (NNT = 3) with 2 positive tests, 12%–21% (NNT = 11–18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.

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“…Urine decoy cells detected by Papanicolaou staining refer to the epithelial cells shed after BKPyV infection (13). The negative predictive value (NPV) of decoy cells for predicting BKPyVAN is close to 100%, but it's positive predictive value (PPV) is only 29% (14,15). The main reason is that decoy cell can be derived from either renal tubular epithelium or ureteral and bladder transitional epithelium (16).…”

Section: Introductionmentioning

confidence: 99%

Detection of Proximal Tubule Involvement by BK Polyomavirus in Kidney Transplant Recipients With Urinary Sediment Double-Immunostaining

et al. 2020

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Background: The extent and depth of BK polyomavirus (BKPyV) infection in renal allograft correlate with prognosis. This study was designed to evaluate the value of urinary sediment double-immunostaining for predicting BKPyV infection in proximal tubular epithelium. Materials and methods: A total of 76 urine sediment cell blocks, as well as the corresponding transplanted kidney tissues with BK polyomavirus associatednephropathy (BKPyVAN), were evaluated by automatic double-immunostaining with anti-58-kDa Golgi protein (58K, a proximal renal tubular marker) + anti-SV40-T and anti-homogentisate 1, 2-dioxygenase (HGD, a renal tubular marker) + anti-SV40-T. Results: Immunohistochemical staining demonstrated that 58K was expressed in proximal tubular epithelium but not in distal tubular epithelium or transitional epithelium. Of the 76 patients, 28 (36.8%) had urinary 58K(+)/SV40-T(+) cells and HGD(+)/SV40-T(+) cells, 41 (53.9%) had only HGD(+)/SV40-T(+) cells, one (1.3%) had only 58K(+)/SV40-T(+) cells, and six (7.9%) had only 58K(−)/HGD(−)/SV40-T(+) cells. The presence of urinary 58K(+)/SV40-T(+) cells was correlated with BKPyV infection in proximal tubular epithelium (P < 0.001, r = 0.806). The mean extent of SV40-T staining was significantly more extensive in patients with urinary 58K(+)/SV40-T(+) cells than those without urinary 58K(+)/SV40-T(+) cells (21.4 vs. 12.0%, P < 0.001). The positive predictive value, negative predictive value, sensitivity, and specificity of urinary 58K(+)/SV40-T(+) cells for predicting BKPyV infection in proximal tubular epithelium

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Screening Algorithm for BK Virus-Associated Nephropathy Using Sequential Testing of Urinary Cytology: A Probabilistic Model Analysis

Cited by 9 publications

References 22 publications

Histological Evolution of BK Virus–Associated Nephropathy: Importance of Integrating Clinical and Pathological Findings

Histological Evolution of BK Virus–Associated Nephropathy: Importance of Integrating Clinical and Pathological Findings

Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Detection of Proximal Tubule Involvement by BK Polyomavirus in Kidney Transplant Recipients With Urinary Sediment Double-Immunostaining

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