Colchicine poisoning can occur not only by taking dosage form but also by ingesting a plant containing colchicine. A 39-year-old man presented to the emergency room with nausea, vomiting, and diarrhea 9 hours after ingestion of wild garlic. Symptoms attributed to food poisoning, and he received supportive cares and discharged. However, he was admitted to the hospital because of severe gastrointestinal presentations 4 hours later. He received treatments based on the diagnosis of acute gastroenteritis. The patient was in a fair condition during 30 hours of hospitalization until he suddenly developed respiratory distress and unfortunately died with cardiopulmonary arrest. The deceased body referred to our legal medicine center for determining cause of death and investigating possible medical staff malpractices. Postmortem examination, autopsy, macropathology and micropathology study, and postmortem toxicological analysis were performed. All results were submitted to the medical committee office for decision. The unknown cause of death was disclosed after determination of colchicine in the plant and botanical identification as Colchicum persicum. The committee determined the most probable cause of death as acute cardiopulmonary complications induced by colchicine poisoning and the manner of death as accidental. The medical staff was acquitted of the malpractice.

Background Determining the required daily number of platelet units in hospitals is a challenging task due to the high uncertainty in daily usage and short shelf life of platelets. Study Design and Methods We developed a linear prediction model to guide the daily ordering quantity of platelet units at a hospital that orders the required units from a central supplier. The predictive model relies on historical demand data and other information from the hospital's information system. The ordering strategy is to place an order at the end of each day to bring the platelet inventory to the predicted demand for the next day. Unlike typical prediction models, the quality of the predictions is measured with respect to the resulting inventory costs of wastage and shortage. We used data from two hospitals in Hamilton, Ontario from 2015 to 2016 to train our model and evaluated its performance based on the resulting wastage and shortage rates in 2017. Results In 2017, respectively 1915 and 4305 platelet units were transfused at the two hospitals, with daily average (SD) usage of 5.2 (3.7) and 11.8 (4.4). The expiry (estimated shortage) rates were 8.67% (13.86%), and 2.28% (8.48%) at the two hospitals, respectively. Our baseline model would have reduced the expiry (shortage) rates to 2.54% (4.01%) and 0.05% (0.44%) for the two hospitals, respectively. Discussion Guiding daily ordering decisions for platelets using our proposed model could lead to a significant reduction of wastage and shortage rates at hospitals.

Platelets are perishable (5–7 day shelf life) blood products required for a variety of clinical treatments. In North America, hospitals typically procure platelet units from a central supplier. As such, the remaining shelf life of the delivered units could be subject to high uncertainty. Our work focuses on developing new models that leverage the increasingly available data from hospital information systems to prescribe ordering decisions in the presence of this uncertainty. Specifically, we consider a periodic review, perishable inventory system with zero lead time and uncertainty in demand and remaining shelf life of orders, operating under an oldest‐unit, first‐out allocation policy. We consider a family of base stock policies and adopt an empirical risk minimization approach to estimate the required inventory at the beginning of each period. The required inventory level for each period is assumed to be a linear function of a set of observed features in that period and the coefficients of the linear model are obtained by minimizing an approximate measure of the in‐sample empirical cost, comprised of a weighted sum of shortage and expiry costs. Our fixed initial age model assumes a constant remaining shelf life for all units. Our robust model assumes that an adversary selects the remaining shelf life of units subject to an uncertainty budget determined through an endogenous uncertainty set. We investigate the out‐of‐sample performance of the proposed models in a case study using data from two Canadian hospitals and in comparison to the hospitals' historical performances as well as other benchmarks. Both models achieve significant improvements over the historical decisions. For instance, the fixed initial age model achieves a 53% and 93% reduction in the expiry rate and an 82% and 99% reduction in the shortage rate for the two hospitals, respectively. Further, it either outperforms or performs as well as the other benchmarks. The robust model achieves better out‐of‐sample generalizability and demonstrates a more “robust” performance under counterfactual remaining age distributions.

No abstract