Subcutaneous sodium valproate in palliative care: A systematic review.

Peer reviewed: TrueBACKGROUND: Seizures are an important palliative symptom, the management of which can be complicated by patients' capacity to swallow oral medications. In this setting, and the wish to avoid intravenous access, subcutaneous infusions may be employed. Options for antiseizure medications that can be provided subcutaneously may be limited. Subcutaneous sodium valproate may be an additional management strategy. AIM: To evaluate the published experience of subcutaneous valproate use in palliative care, namely with respect to effectiveness and tolerability. DESIGN: A systematic review was registered (PROSPERO CRD42023453427), conducted and reported according to PRISMA reporting guidelines. DATA SOURCES: The databases PubMed, EMBASE and Scopus were searched for publications until August 11, 2023. RESULTS: The searches returned 429 results, of which six fulfilled inclusion criteria. Case series were the most common study design, and most studies included <10 individuals who received subcutaneous sodium valproate. There were three studies that presented results on the utility of subcutaneous sodium valproate for seizure control, which described it to be an effective strategy. One study also described it as an effective treatment for neuropathic pain. The doses were often based on presumed 1:1 oral to subcutaneous conversion ratios. Only one study described a local site adverse reaction, which resolved with a change of administration site. CONCLUSIONS: There are limited data on the use of subcutaneous sodium valproate in palliative care. However, palliative symptoms for which subcutaneous sodium valproate have been used successfully are seizures and neuropathic pain. The available data have described few adverse effects, supporting its use with an appropriate degree of caution

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context

Abstract Objective Epilepsy surgery is known to be underutilized. Machine learning‐natural language processing (ML‐NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation. Methods Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of “epilepsy” was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML‐NLP algorithms were then developed using fivefold cross‐validation on the first free‐text clinic note for each patient to identify these criteria. Results There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93–1.0) for this task, sensitivity of 1.0, and specificity of 0.93. Significance This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML‐NLP may assist with the identification of patients suitable for such referral. Plain Language Summary Epilepsy surgery is a beneficial treatment for selected individuals with drug‐resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning‐natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia

sj-pdf-1-pmj-10.1177_02692163241234597 – Supplemental material for Subcutaneous sodium valproate in palliative care: A systematic review

Supplemental material, sj-pdf-1-pmj-10.1177_02692163241234597 for Subcutaneous sodium valproate in palliative care: A systematic review by Sheryn Tan, Jeng Swen Ng, Charis Tang, Brandon Stretton, Joshua Kovoor, Aashray Gupta, Thomson Delloso, Tony Zhang, Rudy Goh, Shaddy El-Masri, Michelle Kiley, Ian Maddocks, Adil Harroud, Sybil Stacpoole, Gregory Crawford and Stephen Bacchi in Palliative Medicine</p

Delayed return of bowel function after general surgery in South Australia

Introduction: Reference ranges for determining pathological versus normal postoperative return of bowel function are not well characterised for general surgery patients. This study aimed to characterise time to first postoperative passage of stool after general surgery; determine associations between clinical factors and delayed time to first postoperative stool; and evaluate the association between delay to first postoperative stool and prolonged length of hospital stay. Methods: This study included consecutive admissions at two tertiary hospitals across a two-year period whom underwent a range of general surgery operations. Multivariable logistic regression analyses were conducted to determine associations between the explanatory variables and delayed first postoperative stool, and between delayed first postoperative stool and length of hospital stay. The previously specified explanatory variables were used, with the addition of the dichotomised ≥4-day delay to first postoperative stool. Prolonged length of hospital stay was considered ≥7 days. Results: 2,212 general surgery patients were included. Median time to first postoperative stool was 2.28 (IQR 1.06–3.96). Median length of stay was 7.19 (IQR 4.50–12.01). Several operative characteristics and medication exposures were associated with delayed first postoperative stool. There was a statistically significant association between delayed first postoperative stool (≥4 days) and prolonged length of stay (≥7 days) (OR 4.34, 95 %CI 3.27 to 5.77, p < 0.001). Conclusions: This study characterised expected reference ranges for time to return of bowel function across various general surgery operations and determined associations with clinical factors that may improve efficiency and identification of pathology within the postoperative course