Clinical predictive models in equine medicine: A systematic review.

Clinical predictive models use a patient's baseline demographic and clinical data to make predictions about patient outcomes and have the potential to aid clinical decision making. The extent of equine clinical predictive models is unknown in the literature. Using PubMed and Google Scholar, we systematically reviewed the predictive models currently described for use in equine patients. Models were eligible for inclusion if they were published in a peer-reviewed article as a multivariable model used to predict a clinical/laboratory/imaging outcome in an individual horse or herd. The agreement of at least two authors was required for model inclusion. We summarised the patient populations, model development methods, performance metric reporting, validation efforts, and, using the Predictive model Risk of Bias Assessment Tool (PROBAST), assessed the risk of bias and applicability concerns for these models. In addition, we summarised the index conditions for which models were developed and provided detailed information on included models. A total of 90 predictive models and 9 external validation studies were included in the final systematic review. A plurality of models (41%) was developed to predict outcomes associated with colic, for example, need for surgery or survival to discharge. All included models were at high risk of bias, defined as failing one or more PROBAST signalling questions, primarily for analysis-related reasons. Importantly, a high risk of bias does not necessarily mean that models are unusable, but that they require more careful consideration prior to clinical use. Concerns about applicability were low for the majority of models. Systematic reviews such as this can serve to increase veterinarians' awareness of predictive models, including evaluation of their performance and their use in different patient populations.

Pre-transplant shedding of BK virus in urine is unrelated to post-transplant viruria and viremia in kidney transplant recipients.

BK virus-(BKV) associated nephropathy (BKVN) is a major cause of allograft injury in kidney transplant recipients. In such patients, subclinical reactivation of latent BKV infection can occur in the pre-transplant period. The purpose of this study was to determine whether urinary BKV shedding in the immediate pre-transplant period is associated with a higher incidence of viruria and viremia during the first year after kidney transplantation. We examined urine samples from 34 kidney transplant recipients, using real-time quantitative polymerase chain reaction to detect BKV. Urine samples were obtained in the immediate pre-transplant period and during the first year after transplant on a monthly basis. If BKV viruria was detected, blood samples were collected and screened for BKV viremia. In the immediate pre-transplant period, we detected BKV viruria in 11 (32.3%) of the 34 recipients. During the first year after transplantation, we detected BKV viruria in all 34 patients and viremia in eight (23.5%). We found no correlation between pre-transplant viruria and post-transplant viruria or viremia (p = 0.2). Although reactivation of latent BKV infection in the pre-transplant period is fairly common among kidney transplant recipients, it is not a risk factor for post-transplant BKV viruria or viremia.

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables.

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Kinematics of the lower extremity.

Range of motion of the lower extremity joints from the hip to the digits will be discussed including both active and passive axis of motion and their planes of motion. The author believes that the interpretation of these ranges of motion is essential in order to properly diagnose and to execute a treatment plan.