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Fulltext Brain and spinal tumour

Goh CH, Lu YY, Lau BL, Oy J, Lee HK, Liew D, et al.

Med J Malaysia, 2014 Dec;69(6):261-7.
PMID: 25934956 MyJurnal

This study reviewed the epidemiology of brain and spinal tumours in Sarawak from January 2009 till December 2012. The crude incidence of brain tumour in Sarawak was 4.6 per 100,000 population/year with cumulative rate 0.5%. Meningioma was the most common brain tumour (32.3%) and followed by astrocytoma (19.4%). Only brain metastases showed a rising trend and cases were doubled in 4 years. This accounted for 15.4% and lung carcinoma was the commonest primary. Others tumour load were consistent. Primitive neuroectodermal tumour (PNET) and astrocytoma were common in paediatrics (60%). We encountered more primary spinal tumour rather than spinal metastases. Intradural schwannoma was the commonest and frequently located at thoracic level. The current healthcare system in Sarawak enables a more consolidate data collection to reflect accurate brain tumours incidence. This advantage allows subsequent future survival outcome research and benchmarking for healthcare resource planning.
Atypical meningioma with nodal metastasis: A case report

Chew SJY, Rajesvaran C, Woo X, Goh CH

Malays J Pathol, 2021 Dec;43(3):453-456.
PMID: 34958067

INTRODUCTION: Meningiomas are common and usually benign central nervous system neoplasms. These neoplasms are graded into three groups which differ in biological behaviour. Atypical meningioma is an intermediate grade (Grade 2) tumour that is rarely associated with metastases compared to higher grade (Grade 3) meningiomas.
CASE REPORT: A 68-year-old lady with a history of multiple craniotomies and hemifacial resections for meningothelial meningioma currently underwent orbital exenteration, tumour debulking and cervical nodal excision for tumour recurrence. Histopathological examination of the tumour showed atypical meningioma, with cervical nodal metastasis.
DISCUSSION: This case report presents a rare finding of lymph node metastasis associated with atypical meningioma. The previous history of surgical resection is a known risk factor for metastasis for low to intermediate grade meningioma. Tumour biology and histology are predictors of metastasis. Haematogenous dissemination is the commonest route of metastasis. No standardised management protocol has been developed and the prognosis remains unknown.
Association between different methods of assessing blood pressure variability and incident cardiovascular disease, cardiovascular mortality and all-cause mortality: a systematic review

Smith TO, Sillito JA, Goh CH, Abdel-Fattah AR, Einarsson A, Soiza RL, et al.

Age Ageing, 2020 02 27;49(2):184-192.
PMID: 31985773 DOI: 10.1093/ageing/afz178

BACKGROUND: Blood pressure variability (BPV) is a possible risk factor for adverse cardiovascular outcomes and mortality. There is uncertainty as to whether BPV is related to differences in populations studied, measurement methods or both. We systematically reviewed the evidence for different methods to assess blood pressure variability (BPV) and their association with future cardiovascular events, cardiovascular mortality and all-cause mortality.
METHODS: Literature databases were searched to June 2019. Observational studies were eligible if they measured short-term BPV, defined as variability in blood pressure measurements acquired either over a 24-hour period or several days. Data were extracted on method of BPV and reported association (or not) on future cardiovascular events, cardiovascular mortality and all-cause mortality. Methodological quality was assessed using the CASP observational study tool and data narratively synthesised.
RESULTS: Sixty-one studies including 3,333,801 individuals were eligible. BPV has been assessed by various methods including ambulatory and home-based BP monitors assessing 24-hour, "day-by-day" and "week-to-week" variability. There was moderate quality evidence of an association between BPV and cardiovascular events (43 studies analysed) or all-cause mortality (26 studies analysed) irrespective of the measurement method in the short- to longer-term. There was moderate quality evidence reporting inconsistent findings on the potential association between cardiovascular mortality, irrespective of methods of BPV assessment (17 studies analysed).
CONCLUSION: An association between BPV, cardiovascular mortality and cardiovascular events and/or all-cause mortality were reported by the majority of studies irrespective of method of measurement. Direct comparisons between studies and reporting of pooled effect sizes were not possible.
Fulltext Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

Goh CH, Ferdowsi M, Gan MH, Kwan BH, Lim WY, Tee YK, et al.

MethodsX, 2024 Jun;12:102508.
PMID: 38162148 DOI: 10.1016/j.mex.2023.102508

Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4-96.1%), specificity of 81.5% (95% CI: 69.8-92.8%) and accuracy of 85.8% (95% CI: 78.6-92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients.