During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

Sensory neuropathy is a common but difficult to quantify complication encountered during treatment of various cancers with taxane-containing regimens. Docetaxel, paclitaxel, and its nanoparticle albuminbound formulation have been extensively studied in randomized clinical trials comparing various dose and schedules for the treatment of breast, lung, and ovarian cancers. This review highlights differences in extent of severe neuropathies encountered in such randomized trials and seeks to draw conclusions in terms of known pharmacologic factors that may lead to neuropathy. This basic knowledge provides an essential background for exploring pharmacogenomic differences among patients in relation to their susceptibility of developing severe manifestations. In addition, the differences highlighted may lead to greater insight into drug and basic host factors (such as age, sex, and ethnicity) contributing to axonal injury from taxanes.

Classic paper on clinical and pharmacological aspects of Cremophor EL and potential advantages of a more pharmacologically predictable formulation.

Since their addition to paclitaxel in the oncologists' armamentarium in the early 1990s, several new taxane formulations have been developed. Besides docetaxel and nab-paclitaxel, new analogs with better therapeutic profiles are being investigated. The goals of this next generation of taxanes are to improve the toxicity profile and efficacy, and to overcome resistance patterns. Several new taxanes, including cabazitaxel, paclitaxel poliglumex, paclitaxel+endotag, and polymeric-micellar paclitaxel, have shown clinical efficacy. These chemotherapeutics are part of many ongoing phase II and III studies on various cancer types. In addition, there are immunotoxins that link key antibodies to mitotic spindle inhibitors (trastuzumab emtansine and brentuximab vedotin). Through this mechanism, novel formulations increase cytotoxicity, improve specificity, and create possibilities for drug enhancement.