Incidence of hydrocephalus following decompressive craniectomy for ischaemic stroke: A systematic review and meta-analysis.

OBJECTIVE: Decompressive craniectomy (DC) following malignant ischaemic stroke is a potentially life-saving procedure. Event rates of ventriculomegaly following DC performed in this setting remain poorly defined. Accordingly, we performed a systematic review to determine the incidence of hydrocephalus and the need for cerebrospinal fluid (CSF) diversion following DC for malignant stroke. METHODS: MEDLINE, EMBASE and Cochrane libraries were searched from database inception to 17 July 2021. Our search strategy consisted of "Decompressive Craniectomy", AND "Ischaemic stroke", AND "Hydrocephalus", along with synonyms. Through screening abstracts and then full texts, studies reporting on rates of ventriculomegaly following DC to treat ischaemic stroke were included for analysis. Event rates were calculated for both of these outcomes. A risk of bias assessment was performed to determine the quality of the included studies. RESULTS: From an initial 1117 articles, 12 were included following full-text screening. All were of retrospective design. The 12 included studies reported on 677 patients, with the proportion experiencing hydrocephalus/ventriculomegaly being 0.38 (95% CI: 0.24, 0.53). Ten studies incorporating 523 patients provided data on the need for permanent CSF diversion, with 0.10 (95% CI: 0.07, 0.13) requiring a shunt. The included studies were overall of high methodological quality and rigour. CONCLUSION: Though hydrocephalus is relatively common following DC in this clinical setting, only a minority of patients are deemed to require permanent CSF diversion. Clinicians should be aware of the incidence of this complication and counsel patients and families appropriately.

Surgery's Rosetta Stone: Natural language processing to predict discharge and readmission after general surgery.

BACKGROUND: This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery. METHODS: Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers. RESULTS: For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression. CONCLUSION: Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.

Sociocultural and Demographic Factors Predict Readmissions for General Surgery Patients.

INTRODUCTION: Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. METHOD: A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1-2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression. RESULTS: There were 12,701 individuals included, with 304 (2.4%) individuals readmitted within 7 days, and 921 (7.3%) readmitted within 30 days. When incorporating absolute values of clinical parameters in the model, age was the only variable significantly associated with 7-day readmission, and primary language and presence of religion were the only variables significantly associated with 30-day readmission. When incorporating change in clinical parameters between the 1-2 days prior to discharge, primary language and religion were predictive of 30-day readmission. When controlling for changes in clinical parameters, only higher comorbidity burden (represented by higher Charlson comorbidity index score) was associated with increased likelihood of 30-day readmission. CONCLUSIONS: Sociocultural and demographic patient factors such as primary language, presence of religion, age, and comorbidity burden predict the likelihood of 7 and 30-day hospital readmission after general surgery. These findings support early implementation a postoperative care model that integrates all biopsychosocial domains across multiple disciplines of healthcare.

Geographical distribution of authorship for leading cardiothoracic surgery journals.

BACKGROUND: Evolution of surgical practice is influenced by publications in the leading journals of that field. If the authorship of a publication lacks geographical diversity, this could create bias and limit generalizability of the evidence. Accordingly, we conducted a geographical analysis of the leading Cardiothoracic Surgery journals worldwide. METHODS: Using 2020 Impact Factor, we searched the leading Cardiothoracic Surgery journals over the past decade. Only original articles were included. Data regarding first, second and last authors were extracted from every article. From this, we analysed country of affiliation, highest academic degree obtained and author location by metropolitan or rural setting. RESULTS: A total of 12,706 original articles were published in the top 5 ranked Cardiothoracic journals between 2011 and 2020. Authors originated from 69 countries, with the majority being from North America and Western Europe. The United States was the most common country of affiliation (42.8%) in all five journals, with New York City the most prominent city. A total of  63.7% of the authorship originated from large metropolitan areas (estimated as population greater than 500,000 residents), and the most common degrees obtained by authors were MD and PhD. CONCLUSION: The prominent Cardiothoracic authorship is predominantly located in Western countries, most commonly large metropolitan centers in the United States. This raises questions as to whether the literature adequately reflects populations in other geographical areas such as the continents of South America and Africa and rural settings. Leading journals should consider policies which encourage publication by authors from geographical locations that are underrepresented globally.