Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.     

Addressing Emergency Department Issues Using Advanced Practice in Saudi Arabia

Emergency departments (EDs) struggle to manage safe, effective care in the face of increasing patient volume. The role of the nurse practitioner has been shown to improve key factors such as waiting times and the patients’ experience in the ED. This quality improvement project evaluates the effectiveness of a nurse practitioner based in the ED. One nurse practitioner managed patients presenting with minor trauma over 3 months. A pre-post intervention approach was implemented and evaluated. Over 3 months, waiting times and the number of patients who left without being seen were reduced.     

Primary dural lymphoma: A novel concept of heterogeneous disease

Spinal primary dural lymphoma (PDL) is uncommon with a total of 37 previous well‐documented cases reported, including one diagnosed in the authors' institution. More recently we encountered an additional case of spinal PDL that, similarly to our previous case, was grade 1–2 follicular B‐cell PDL. Our two cases were diagnosed over a 3‐year interval in a 72‐year‐old female and a 74‐year‐old male, respectively. An exhaustive literature review on PDL was performed consequently to reveal that: (i) spinal and cerebral sites of involvement by PDL are constantly mutually exclusive; and (ii) unlike cerebral PDL, which is usually of marginal zone B‐cell type, only two of the 38 cases of spinal PDL were diagnosed as such, diffuse large B‐cell lymphoma being the most commonly encountered type in the spine. This divergence infers that, in contrast to the prevailing concept that PDL is a unique disease group, PDL appears to be rather heterogeneous with a difference in predilection of lymphoma type for the anatomical site of dural involvement. Such a site‐specific lymphoma‐type predilection phenomenon, well‐recognized in other organ systems, has not been acknowledged in PDL. This report brings new insights into PDL, and may contribute to a better understanding of nervous system pathophysiology and lymphoma classification.