...when evaluating the extended Glasgow Outcome Scale (GOSE), we addressed the time-dependent nature of NPi by a summary measure of the longitudinal NPi profile. The focus of our study was not on ...making dynamic predictions as to long-term outcomes based on the NPi values in the first week in an intensive care unit, as 1 week is not sufficient time to do so. ...this approach might prove useful in predicting the short-term prognosis in an intensive care unit or hospital setting, and we are actively working on this approach.
The Banff Classification of Allograft Pathology is an international consensus classification for the reporting of biopsies from solid organ transplants. Since its initial conception in 1991 for renal ...transplants, it has undergone review every 2 years, with attendant updated publications. The rapid expansion of knowledge in the field has led to numerous revisions of the classification. The resultant dispersal of relevant content makes it difficult for novices and experienced pathologists to faithfully apply the classification in routine diagnostic work and in clinical trials. This review shall provide a complete and simple illustrated reference guide of the Banff Classification of Kidney Allograft Pathology based on all publications including the 2017 update. It is intended as a concise desktop reference for pathologists and clinicians, providing definitions, Banff Lesion Scores and Banff Diagnostic Categories. An online website reference guide hosted by the Banff Foundation for Allograft Pathology (www.banfffoundation.org) is being developed, which will be updated with future refinement of the Banff Classification from 2019 onward.
Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.
Participants without ...atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.
We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval CI, 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.
The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
: Prebiopsy multiparametric magnetic resonance imaging (mpMRI) is increasingly used in prostate cancer diagnosis. The reported negative predictive value (NPV) of mpMRI is used by some clinicians to ...aid in decision making about whether or not to proceed to biopsy.
: We aim to perform a contemporary systematic review that reflects the latest literature on optimal mpMRI techniques and scoring systems to update the NPV of mpMRI for clinically significant prostate cancer (csPCa).
: We conducted a systematic literature search and included studies from 2016 to September 4, 2019, which assessed the NPV of mpMRI for csPCa, using biopsy or clinical follow-up as the reference standard. To ensure that studies included in this analysis reflect contemporary practice, we only included studies in which mpMRI findings were interpreted according to the Prostate Imaging Reporting and Data System (PIRADS) or similar Likert grading system. We define negative mpMRI as either (1) PIRADS/Likert 1–2 or (2) PIRADS/Likert 1–3; csPCa was defined as either (1) Gleason grade group ≥2 or (2) Gleason grade group ≥3. We calculated NPV separately for each combination of negative mpMRI and csPCa.
: A total of 42 studies with 7321 patients met our inclusion criteria and were included for analysis. Using definition (1) for negative mpMRI and csPCa, the pooled NPV for biopsy-naïve men was 90.8% (95% confidence interval CI 88.1–93.1%). When defining csPCa using definition (2), the NPV for csPCa was 97.1% (95% CI 94.9–98.7%). Calculation of the pooled NPV using definition (2) for negative mpMRI and definition (1) for csPCa yielded the following: 86.8% (95% CI 80.1–92.4%). Using definition (2) for both negative mpMRI and csPCa, the pooled NPV from two studies was 96.1% (95% CI 93.4–98.2%).
: Multiparametric MRI of the prostate is generally an accurate test for ruling out csPCa. However, we observed heterogeneity in the NPV estimates, and local institutional data should form the basis of decision making if available.
: The negative predictive values should assist in decision making for clinicians considering not proceeding to biopsy in men with elevated age-specific prostate-specific antigen and multiparametric magnetic resonance imaging reported as negative (or equivocal) on Prostate Imaging Reporting and Data System/Likert scoring. Some 7–10% of men, depending on the setting, will miss a diagnosis of clinically significant cancer if they do not proceed to biopsy. Given the institutional variation in results, it is of upmost importance to base decision making on local data if available.
Depending on definition of “negative multiparametric magnetic resonance imaging” and “clinically significant prostate cancer”, the negative predictive value ranged from 84% to 97%, suggesting that it is a reliable test to rule out clinically significant prostate cancer.
Approximately 20%-35% of individuals 12-35 years old who meet criteria for a prodromal risk syndrome convert to psychosis within 2 years. However, this estimate ignores the fact that clinical ...high-risk cases vary considerably in risk. The authors sought to create a risk calculator, based on profiles of risk indicators, that can ascertain the probability of conversion to psychosis in individual patients.
The study subjects were 596 clinical high-risk participants from the second phase of the North American Prodrome Longitudinal Study who were followed up to the time of conversion to psychosis or last contact (up to 2 years). The predictors examined were limited to those that are supported by previous studies and are readily obtainable in general clinical settings. Time-to-event regression was used to build a multivariate model predicting conversion, with internal validation using 1,000 bootstrap resamples.
The 2-year probability of conversion to psychosis was 16%. Higher levels of unusual thought content and suspiciousness, greater decline in social functioning, lower verbal learning and memory performance, slower speed of processing, and younger age at baseline each contributed to individual risk for psychosis. Stressful life events, trauma, and family history of schizophrenia were not significant predictors. The multivariate model achieved a concordance index of 0.71 and, as reported in an article by Carrión et al., published concurrently with this one, was validated in an independent external data set. The results are instantiated in a web-based risk prediction tool envisioned to be most useful in research protocols involving the psychosis prodrome.
A risk calculator comparable in accuracy to those for cardiovascular disease and cancer is available to predict individualized conversion risks in newly ascertained clinical high-risk cases. Given that the risk calculator can be validly applied only for patients who screen positive on the Structured Clinical Interview for Psychosis Risk Syndromes, which requires training to administer, its most immediate uses will be in research on psychosis risk factors and in research-driven clinical (prevention) trials.
PDF
