Objective Develop a process map of when patients learn about their proposed surgery and what resources patients use to educate themselves. Design A mixed methods design, combining semistructured stakeholder interviews, quantitative validation using electronic healthcare records (EHR) in a retrospective cohort and a cross-sectional patient survey. Setting A single surgical centre in the UK. Participants Fourteen members of the spinal multidisciplinary team were interviewed to develop the process map. This process map was validated using the EHR of 50 patients undergoing elective spine surgery between January and June 2022. Postprocedure, feedback was gathered from 25 patient surveys to identify which resources they used to learn about their spinal procedure. Patients below the age of 18 or who received emergency surgery were excluded. Interventions Elective spine surgery and patient questionnaires given postoperatively either on the ward or in follow-up clinic. Primary and secondary outcome measures The primary outcome was the percentage of the study cohort that was present at encounters on the process map. Key timepoints were defined if >80% of patients were present. The secondary outcome was the percentage of the study cohort that used educational resources listed in the patient questionnaire. Results There were 342 encounters which occurred across the cohort, with 16 discrete event categories identified. The initial surgical clinic (88%), anaesthetic preoperative assessment (96%) and admission for surgery (100%) were identified as key timepoints. Surveys identified that patients most used verbal information from their surgeon (100%) followed by written information from their surgeon (52%) and the internet (40%) to learn about their surgery. Conclusions Process mapping is an effective method of illustrating the patient pathway. The initial surgical clinic, anaesthetic preoperative assessment and surgical admission are key timepoints where patients receive information. This has future implications for guiding patient education interventions to focus at key timepoints.
Abstract Accurate intra‐operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration. This article presents PitRSDNet for predicting RSD during pituitary surgery, a spatio‐temporal neural network model that learns from historical data focusing on workflow sequences. PitRSDNet integrates workflow knowledge into RSD prediction in two forms: (1) multi‐task learning for concurrently predicting step and RSD; and (2) incorporating prior steps as context in temporal learning and inference. PitRSDNet is trained and evaluated on a new endoscopic pituitary surgery dataset with 88 videos to show competitive performance improvements over previous statistical and machine learning methods. The findings also highlight how PitRSDNet improves RSD precision on outlier cases utilising the knowledge of prior steps.
There is interest in identifying reliable prognostic biomarkers in schizophrenia and related disorders. Serum inflammatory markers, such as white cell count and C-reactive protein (CRP), have been shown to be elevated during psychotic episodes; however, their pathogenic role is uncertain. There is limited data relating to their variability in clinical practice and relationship to clinical outcome. We have sought to investigate whether routine clinical case records contain the necessary data to further understand the relationship between serum inflammatory markers and prognosis.
Method
This is a retrospective case note review of patients admitted to an inner city female acute psychiatry ward. Cases were identified by reviewing electronic ward round records. Patients included had a diagnosis of non-affective, non-drug induced psychosis (schizophrenia, acute and transient psychotic disorder, persistent delusional disorder, schizotypal disorder and nonorganic psychosis) and had received a routine admission blood test. Exclusion criteria included pregnancy, significant recreational drug use prior to admission, clinical evidence of infection or history of inflammatory or haematological disease.
Results
A total of 20 patients met the inclusion criteria between April 2015 and October 2016. Mean age was 43 years (SD=15) and the most common ethnicities were White British (23%), Mixed Ethnicity (23%), and Caribbean (23%). The majority of cases were detained under the Mental Health Act (68%) and had previously been treated with antipsychotic medication (94%). Mean admission duration was 38 days (SD=30) and average time from admission to routine admission blood test was 4 days (SD=3 days). Admission duration was moderately positively correlated with white cell count (r=0.41, n=20, p=0.07), platelet count (r=0.40, n=20, p=0.08) and albumin (r=0.42, n=20, p=0.07). Admission duration was weakly correlated with neutrophil count (r=0.27, n=20, p=0.24) and CRP (r=−0.22, n=20, p=0.35). When patients split according to those above and below the median admission length, patients with longer admissions had significantly higher platelet count (p<0.05) only.
Conclusion
This small retrospective review suggests that in routine clinical case practice information is collected which could be used to explore the role of inflammatory markers as prognostic biomarkers in patients with schizophrenia and related disorders. Despite our extremely small sample we have found a positive correlation between platelet count and admission length. However this needs to be considered in the presence of multiple confounders. Use of electronic patient databases may be helpful in extending the sample to formally establish any prognostic relationships.
"How does the publication fate of abstracts presented at the Society of British Neurological Surgeons meetings differ five years on?." British Journal of Neurosurgery, 31(3), pp. 291–292
Abstract Purpose Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods A data-driven analysis was performed using all available electronic neurosurgical referrals (2006-2022) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm and Prophet. Mean absolute, and percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results 462 of 36224 emergency referrals were included (referring centres=48; mean patient age=56.7-years, female:male=0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM and Prophet algorithms across scoring metrics, with standard accuracy being achieved for 6-monthly and yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p<0.001). Conclusion This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimating future demand and highlighting areas for service improvement.
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