Conectar
To characterise patient and medication-related patterns observed in drug-related pressure ulcers (DRPUs) and provide descriptive findings that may support future consensus-building.
Multicentre retrospective observational study.
20 hospitals across Japan participated in the study with hospital pharmacists specialised in PU care.
A total of 1113 hospitalised patients with existing PUs were included and classified into three groups (definite, probable and no-possibility of DRPUs) based on predefined criteria.
The primary outcome was the description of medication-related characteristics observed in each DRPU classification group, including polypharmacy, initiation of new medications and dose adjustments. Secondary outcomes included differences in ulcer characteristics and functional status across DRPU categories.
The definite group (n=128, 11.5%) showed a significantly higher prevalence of polypharmacy (83.6% vs 71.1% in the no-possibility group, p
Medication-related characteristics such as polypharmacy, initiation of new medications, dose modifications and use of antipsychotics were more frequently observed in the definite DRPU group. These descriptive findings may help characterise the clinical patterns of DRPUs and may inform future hypothesis generation.
by Masatsugu Ishii, Osamu Itano, Hideki Iwamoto, Yuko Takami, Naomi Okada, Tetsuya Inoue, Satoshi Itano
We identified an effective chemotherapy regimen in patients refractory to standard chemotherapy. We included patients with unresectable colorectal liver metastases who underwent hepatic artery infusion chemotherapy and systemic chemotherapy between January 2015 and December 2022. This study was a retrospective analysis conducted at a single center. The patients received either biweekly oxaliplatin and 5-fluorouracil through hepatic artery infusion chemotherapy as well as bevacizumab and leucovorin injected intravenously (HAIC-FOLFOX-B) or biweekly irinotecan and 5-fluorouracil by hepatic artery infusion chemotherapy and bevacizumab and leucovorin injected intravenously (HAIC-FOLFIRI-B). Of the 42 patients, 20 underwent HAIC-FOLFOX-B while 22 underwent HAIC-FOLFIRI-B treatment with response rates of 25% and 4.5%, respectively. The median overall survival and progression-free survival were 12.9 and 4.7 months and 17.4 and 7.7 months in patients undergoing HAIC-FOLFOX-B and HAIC-FOLFIRI-B, respectively. The overall incidence of grade 3/4 toxicity was 23.8%. However, no treatment-related deaths occurred. Functional catheter-associated problems occurred in 9.5% of the patients. Hepatic arterial occlusion occurred in three patients (7.1%); catheter-associated infection occurred in one (2.4%) patient. However, these occurrences were not life-threatening complications. HAIC-FOLFOX-B and HAIC-FOLFIRI-B might improve survival in patients with unresectable colorectal liver metastases and in those who underwent both systemic oxaliplatin-based and irinotecan-based chemotherapies and were refractory to them. HAIC FOLFOX-B and FOLFIRI-B regimens might be effective therapeutic options in patients with unresectable colorectal liver metastases refractory to standard systemic chemotherapy.
There are challenges involving human resource management, as the selection and evaluation processes for nursing diagnostic labels are time-consuming, resulting in an excessive workload. This, in turn, can lead to insufficient attention being given to patients' medical issues. As a proof of concept, to solve challenges related to nursing diagnoses, we developed an artificial neural network model using progress records and evaluated its performance. Specifically, datasets were obtained from progress record data from the critical care department system in Japan between 2014 and 2019 and the corresponding nursing diagnosis data from electronic medical records. The model was trained, and its performance was evaluated. We compared several methods for vectorizing progress records and evaluated performance with and without oversampling for imbalanced data. We used a naive Bayes classifier for comparison. The model using term frequency–inverse document frequency achieved the highest values for both accuracy and the area under the precision-recall curve across all target nursing diagnoses (accuracy = 0.705–0.911; area under the precision-recall curve = 0.387–0.929). The artificial neural network model outperformed the naive Bayes classifier in both accuracy and area under the precision-recall curve, which indicated its superiority as a classifier. 
FreshRSS 