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Fulltext Widespread pandemic, wide-ranging response: student-led virtual telemedicine and digital community-based healthcare provision in a university healthcare centre in response to coronavirus quarantine measures

Muhammad Syafiq Abdullah, Nicholas Pang, Sharinah Abd Kassim, Flora Aurora AmatUdat, NurZiana Ulkaya, NuraisyahNurullah, et al.

Malaysian Journal of Medicine and Health Sciences, 2020;16(105):13-0.
MyJurnal

Introduction: Isolation and border control measures, with home quarantine measures, are essential to stem the spread of the newly emergent novel Covid-19. Such measures are doomed to fail if reliant on traditional isolation methods, which entail small numbers of overworked healthcare staff screening and surveilling large numbers of well individuals who are potential false positives. Innovative method employed by Hospital UMS to overcome these logistics difficulties. Methods: A total of 440 returning China students to UMS were planned for home quarantine measures for 14 days. In the intervening 14 days, groups of ten quarantined individuals were assigned to 1 Manda- rin-speaking medical student liaison officer (LO). LOs performed assessment toolkit for 14 consecutive days virtually via WhatsApp and WeChat and reported back to NCOV central command if any symptoms ensued. Results: 45 China students have been put on home quarantine. Two (2) students with symptoms were monitored virtually till resolution of symptoms. Also, five (5) students with uncontactable phone numbers required tracking down, using var- ious methods eg: retrieval from close contacts through wide-bore virtual search. No cases so far have been positive for NCOV or have required referral to tertiary hospitals. Qualitatively, such methods are a vital public health inter- vention, as task shifting happens to semi-professionals. Hospital UMS first trial of two cherished founding principles: community-based rather than healthcare-facility center healthcare delivery, and judicious use of digital health com- munications, applications, and rudimentary telemedicine. Conclusion: Student-led virtual telemedicine and digital health delivery has potential in public health crises like NCOV, freeing frontline healthcare staff to devote energies to their specialties of screening and treatment. Integration of video and biometrics to incorporate true telemedicine, allowing individuals to be “hospitalized” in a community setting in situations of low risk.

Matched MeSH terms: Biometry
Fulltext Comparison of dento-alveolar and skeletal changes between immediate and delayed placement of fixed appliances after twin block therapy

Aisha Maqsad Hussain, Gururajaprasad Kaggal Lakshmana Rao, Mohd Fadhli Khamis, Norehan Mokhtar

Malaysian Journal of Medicine and Health Sciences, 2020;16(3):231-237.
MyJurnal

Introduction: A parallel design randomized clinical trial was conducted to compare dentoalveolar and skeletal changes in two groups of patients who had completed twin block therapy; one group had a three-month night-time retention period whereas the other group had no retention period, after twin block therapy but before fixed applianc- es. Methods: 26 participants of Malay ethnicity aged 10 to 15 years were included in the trial and had an overjet of 5mm or greater, molar relationship greater than half cusp Class II on a skeletal Class II base which had been corrected to a Class I molar relationship following twin block therapy. Following randomization, the 26 were divided into two groups of 13. Group A had fixed appliances bonded immediately whereas group B continued wearing twin block at night for three months, after which fixed appliances were bonded. Lateral cephalograms assessed were those taken before randomization, upon twin block therapy completion (T1) and six months after bond-up of fixed appliances (T2). Results: Paired t-test showed several statistically significant dentoalveolar and skeletal changes in group A. In contrast, only condylar head position exhibited a statistically significant change in group B. Despite a statistical sig- nificance, changes measured in both groups were minimal at less than 2mm and therefore clinically insignificant. Independent t-test showed no statistically significant difference between the changes recorded in both groups. Con- clusion: The results suggest that a three-month night-time retention period after twin block therapy does not lead to any changes that may be considered clinically beneficial.

Matched MeSH terms: Biometry
Fulltext Universiti Malaysia Sabah student nurses’ perceived readiness in caring for patients infected with Coronavirus

Segaran Ramodran, Yeap Boon Tat, Norkiah Saat, Constance Liew Sat Lin, Nur Atikah Md Taib, Symeon Mandrinos

Malaysian Journal of Medicine and Health Sciences, 2020;16(105):49-0.
MyJurnal

Introduction: Recent Coronavirus outbreak has raised concern among student nurses who are doing their clinical posting tenure regarding the risk ofpatient acquired infection. This study examined perceived readiness to pro- vide coronavirus patient careduring clinical posting among student nurses in UMS. Method: This study deployed a cross-section survey design using a self-rated questionnaire to evaluate respondents’ level of readiness towards Coronavirus patient care. A total of 177 (N) respondents comprising of nursing students from UMS (year 1 n=55, year 2 n=56, year 3 n=66) participated in the study. The study questionnaire captured demographics and comprised of 15Likert -scale items that assessed the level of perceived readiness to provide Coronavirus patient care. The ques- tionnaire was adapted from a previous SARs pandemic studyand revalidated within the local context (α = 0.78). Data analysis used descriptive statistics by frequency counts and Fisher exact test for demographic correlates with the level of readiness. Results: Among 177 student nurses in this study68% (n= 121) were willing and ready to provide Coronavirus patient care and 32% (n = 56) were hesitant or not willing. Regarding confidence of safety using PPE to provide patient care, 34 % (n = 61) of respondents rated not confident and deemed it hazardous even with full PPE use. On the issue of if legally mandated to care for Coronavirus patients, 22% were hesitant to provide care and will considerleaving nursing training if compelled to do so. There was a significantly lower perceived level of readiness towards Coronavirus patient care among 1st-year student nurses and those who had not attended any Coronavirus educational session (p= 0.06). Conclusion: The findings indicate although the majority of student nurses are willing to provide care for Coronavirus infected patients during their clinical posting tenure, a small proportion of student’s nurses were hesitant.

Matched MeSH terms: Biometry
Fulltext Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification

Haque F, Bin Ibne Reaz M, Chowdhury MEH, Srivastava G, Hamid Md Ali S, Bakar AAA, et al.

Diagnostics (Basel), 2021 Apr 28;11(5).
PMID: 33925190 DOI: 10.3390/diagnostics11050801

BACKGROUND: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature.
METHOD: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms.
RESULTS: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs.
CONCLUSIONS: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.

Matched MeSH terms: Biometry
Fulltext The Effect of 5-Min Mindful Breathing on Pain in Palliative Care Cancer Patients: A Randomized Controlled Study

Guan NC, Beng TS, Sue-Yin L, Kanagasundram S

Indian J Palliat Care, 2021 02 17;27(1):83-88.
PMID: 34035622 DOI: 10.4103/IJPC.IJPC_122_20

Context: While pain is a common complaint among palliative cancer patients, there is little research looking into nonpharmacological methods for the reduction of pain in the palliative setting.
Aim: This study aims to study the efficacy of 5-min mindful breathing for rapid reduction of pain in a palliative care setting.
Methods: This is a sub-analysis of the previous randomized controlled study on distress reduction. Sixty patients were recruited and randomly assigned to either the intervention (5-min mindful breathing) or the control (5-min normal listening) group. Participants reported their pain on a 10-item analog scale at baseline, immediately after intervention and 10 min postintervention. Changes in pain scores were further analyzed.
Results: Pain scores decreased for both the intervention and control groups. However, the reduction of pain did not reach statistical difference in both groups (P > 0.05).
Conclusion: Five-minute mindful breathing is a quick and easy to administer therapy but does not have significant effects in terms of pain reduction in palliative settings. Future research and directions are nonetheless suggested and encouraged to look for short-term mindfulness-based therapies on pain reduction for this population.

Matched MeSH terms: Biometry
Fulltext Strategic roles of industrial statistics in modern industry

Djauhari, M.A.

ASM Science Journal, 2011;5(1):53-63.
MyJurnal

Industrial statistics is an important part of the management system in any industry that strives to continuously improve quality and increase productivity and efficiency. That system covers supply chain management, production design and prototyping, production process and marketing. Industrial statisticians, industrial engineers and industrial leaders should work together hand in hand, in the same language, to ensure that the process and products are as expected. The system itself is never complete. Thus, the usefulness, manageability and reliability of all statistical models used in the system are to be considered as first priority, but those skills are not sufficient. Industrial statisticians should also, of course, be able to come and go between the two poles: statistics and industry. This requirement needs a good understanding about the culture of these poles and how to conduct a mutual symbiosis. One of the principal bridges between these cultures is statistical process control (SPC). This paper is to show that modern industry cannot escape from SPC, especially in a multivariate setting. This setting, which characterizes modern industry, consists of two philosophical problems: how to order data and how to measure process variability. Our recent research results sponsored by the Government of Malaysia will be presented to illustrate the challenging statistical problems in modern industry.

Matched MeSH terms: Biometry
Fulltext Classification of Partial Discharge Measured under Different Levels of Noise Contamination

Jee Keen Raymond W, Illias HA, Abu Bakar AH

PLoS One, 2017;12(1):e0170111.
PMID: 28085953 DOI: 10.1371/journal.pone.0170111

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Matched MeSH terms: Biometry
A Comparison Between Bayesian and Maximum Likelihood Estimations in Estimating Finite Mixture Model for Financial Data

Seuk-Yen Phoong, Mohd Tahir Ismail

Sains Malaysiana, 2015;44:1033-1039.

Over the years, maximum likelihood estimation and Bayesian method became popular statistical tools in which applied to fit finite mixture model. These trends begin with the advent of computer technology during the last decades. Moreover, the asymptotic properties for both statistical methods also act as one of the main reasons that boost the popularity of the methods. The difference between these two approaches is that the parameters for maximum likelihood estimation are fixed, but unknown meanwhile the parameters for Bayesian method act as random variables with known prior distributions. In the present paper, both the maximum likelihood estimation and Bayesian method are applied to investigate the relationship between exchange rate and the rubber price for Malaysia, Thailand, Philippines and Indonesia. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of time series data, Akaike Information Criterion and Bayesian Information Criterion are adopted in this paper. The result depicts that the Bayesian method performs better than maximum likelihood estimation on financial data.

Matched MeSH terms: Biometry
Fulltext Tahap keyakinan diri bekas penagih dadah dalam pengawasan di malaysia: ke arah kepulihan menyeluruh (self-esteem among former drug addicts under observation in Malaysia: towards a comprehensive recovery)

Salina N, Fauziah I, Ezarina Z, Mohd Norahim Mohamed S, Nor Jana S

Jurnal Psikologi Malaysia, 2017;31:29-39.

Recovering drug addict is affected by two main factors, namely internal factors (such as resilience and self-confidence) and external (support from families, employers, friends, and community). One of the internal factors that appear to influence the level of recovery of former drug addict is selfconfidence. Therefore this study aims to measure the level of self-confidence among former drug addicts, also known as Orang Kena Pengawasan (OKP) who underwent rehabilitation in 6-11 months and 12-24 months. The study was conducted using cross-sectional surveys. A total of 386 former drug addicts in Peninsular Malaysia were involved in this study with 198 respondents undergoing 6-11 months rehabilitation programme and 197 were respondents who were released within 12-24 months. The data obtained were analyzed using descriptive statistics. This analysis was used to measure the level of self-confidence between respondents who underwent the 6-11 months rehabilitation programme (Group 1) and 12-24 months (Group 2). The study found that the majority of both groups showed no signiticant difference in the level of recovery from the aspect of self-confidence. The study also found the majority of respondents of both groups showed a moderate level of confidence of 58.5 percent. However, Group 1 showed a higher percentage of self-confidence (63.5%) compared to Group 2 of (53.8%). The findings have implications for the development of strategies towards a strong self-confidence among the inmates to reduce recidivism rates in Malaysia.

Matched MeSH terms: Biometry
Otolith shape analyses and dimensions of the anchovy engraulis encrasicolus l. In the black and marmara seas

Melek Zeng?n, Semra Sayg?n, Nazm? Polat

Sains Malaysiana, 2015;44:657-662.

Otoliths, which can be used for the evaluation of relationships between the environment and organisms, are structures
consisting of calcium carbonate. The aim of this study was to realize the shape analysis. In addition, it is to detect the
characteristics of otolith biometrics in order to determine the relationship between the fish size of Engraulis encrasicolus
L. from the Black and Marmara Seas. The samples were obtained from the Black and Marmara Seas between December
2013 and February 2014. The relationships between the TL (Total length) and OL (Otolith length), TL and OB (Otolith
breadth), and TL and OW (Otolith weight) were determined using the linear regression equation. Form factor, roundness,
circularity and rectangularity were used for shape analyses. According to the data, there was no difference between
localities (p>0.05). Moreover, there was no difference between the left and right otoliths of the individuals sampled from
the same locality (p>0.05). According to the regression coefficient for relationships of TL-OL, TL-OB and TL-OW, otolith
length was identified as the best index for estimating fish length (r
2
>0.70). It showed that index values were statistically
different between two populations (p<0.001).

Matched MeSH terms: Biometry
Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review

Aburas MM, Ahamad MSS, Omar NQ

Environ Monit Assess, 2019 Mar 05;191(4):205.
PMID: 30834982 DOI: 10.1007/s10661-019-7330-6

Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.

Matched MeSH terms: Biometry
Fulltext Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

Khan MJH, Hussain MA, Mujtaba IM

Materials (Basel), 2014 Mar 27;7(4):2440-2458.
PMID: 28788576 DOI: 10.3390/ma7042440

Propylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75 °C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR).

Matched MeSH terms: Biometry
Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms

Malik A, Tikhamarine Y, Sammen SS, Abba SI, Shahid S

Environ Sci Pollut Res Int, 2021 Mar 22.
PMID: 33751346 DOI: 10.1007/s11356-021-13445-0

Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.

Matched MeSH terms: Biometry
Fulltext Low complexity smart grid security protocol based on elliptic curve cryptography, biometrics and hamming distance

Mutlaq KA, Nyangaresi VO, Omar MA, Abduljabbar ZA, Abduljaleel IQ, Ma J, et al.

PLoS One, 2024;19(1):e0296781.
PMID: 38261555 DOI: 10.1371/journal.pone.0296781

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols.

Matched MeSH terms: Biometry
Darkroom prone provocative test in primary angle closure glaucoma relatives

Zahari M, Ong YM, Taharin R, Ramli N

Optom Vis Sci, 2014 Apr;91(4):459-63.
PMID: 24637481 DOI: 10.1097/OPX.0000000000000220

To evaluate ocular biometric parameters and darkroom prone provocative test (DPPT) in family members of primary angle closure (PAC) glaucoma (PACG) patients and to establish any correlation between these biometric parameters and the DPPT response.

Matched MeSH terms: Biometry/methods
Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Ghanizadeh A, Abarghouei AA, Sinaie S, Saad P, Shamsuddin SM

Appl Opt, 2011 Jul 1;50(19):3191-200.
PMID: 21743518 DOI: 10.1364/AO.50.003191

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

Matched MeSH terms: Biometry/methods*
Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs

Teoh AB, Goh A, Ngo DC

IEEE Trans Pattern Anal Mach Intell, 2006 Dec;28(12):1892-901.
PMID: 17108365

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.

Matched MeSH terms: Biometry/methods*
Investigating the biometric and physicochemical characteristics of freshly harvested Pacific white shrimp (Litopenaeus vannamei): a comparative approach

Okpala CO, Bono G

J Sci Food Agric, 2016 Mar 15;96(4):1231-40.
PMID: 25866918 DOI: 10.1002/jsfa.7211

The practicality of biometrics of seafood cannot be overemphasized, particularly for competent authorities of the shrimp industry. However, there is a paucity of relevant literature on the relationship between biometric and physicochemical indices of freshly harvested shrimp. This work therefore investigated the relationship between biometric (standard length (SL), total weight (TW) and condition factor (CF)) and physicochemical (moisture content, pH, titratable acidity, water activity, water retention index, colour values and fracturability) characteristics of freshly harvested Pacific white shrimp (Litopenaeus vannamei) obtained from three different farms. The relationships between these parameters were determined using correlation and regression analyses.

Matched MeSH terms: Biometry*
Fulltext Ocular biometry in the subtypes of primary angle closure glaucoma in University Malaya Medical Centre

Mimiwati Z, Fathilah J

Med J Malaysia, 2001 Sep;56(3):341-9.
PMID: 11732081

Thirty-seven consecutive patients (41 eyes) diagnosed with primary angle closure glaucoma (PACG) attending the Glaucoma Clinic in University Malaya Medical Centre, over a period of 6 months were categorized into acute, subacute and chronic PACG from their clinical presentation. Each case was subjected to automated refraction, A-scan biometry for anterior chamber depth, axial length and lens thickness, keratometry and corneal diameter measurement. Calculations for the relative lens position and the lens thickness: axial length index were performed. The data collected was analysed by the nonparametric test (Kruskal-Wallis), one way analysis of variance (ANOVA), chi-square test, Spearman's nonparametric correlations and regression analysis. For controls 15 eyes from 15 normal subjects matched for age, sex, refractive error and race were chosen and subjected to the same examinations. Chronic PACG was the predominant subtype (53.6% of patients and 58.5% of eyes). The ocular biometric measurements of acute PACG eyes deviated most from normals in having the shallowest anterior chamber depth, shortest axial length, smallest corneal diameter, steepest corneal radius, thickest and most anteriorly situated lens, and the greatest lens thickness: axial length index. The subacute subtype was closest to normal and chronic PACG subtype fell in between in most of the biometric characteristics. These findings were not statistically significant. All PACG eyes as a group however showed statistically significant shallower anterior chamber depth (p < 0.05), and a more anterior relative lens position (p < 0.05) compared to normals.

Matched MeSH terms: Biometry*
Fulltext Evaluation of Refractive Status and Ocular Biometric Parameters in Primary Angle Closure Disease

Loh CC, Kamaruddin H, Bastion MC, Husain R, Mohd Isa H, Md Din N

Ophthalmic Res, 2021;64(2):246-252.
PMID: 32810853 DOI: 10.1159/000510925

INTRODUCTION: The aim of the study was to evaluate the refractive status and ocular biometric parameters in subjects with angle closure in Malaysia.
METHODS: This cross-sectional study was conducted on 171 primary angle closure patients (268 eyes). Visual acuity, refraction, and ocular biometry (central anterior chamber depth [ACD], axial length [AL], and lens thickness) were recorded. Vitreous cavity length (VL) and relative lens position (RLP) were calculated.
RESULTS: A total of 92 Primary Angle Closure Suspect (PACS), 30 Primary Angle Closure (PAC), and 146 Primary Angle Closure Glaucoma (PACG) eyes were included. Chinese ethnicity formed the majority (n = 197, 73.5%), followed by Malay (n = 57, 21.3%) and Indian (n = 14, 5.2%). There was a significant female preponderance with a female to male ratio of 1.85. Mean age was 65.7 ± 7.7 years. Mean spherical equivalent was +0.33 ± 1.29 D. Approximately half (n = 137, 51%) of the eyes were hyperopic (spherical power ≥+0.5), with PACG having the highest percentage of hyperopia (n = 69, 50.4%). Myopia and emmetropia were present in 48 (17.9) and 83 (31%) eyes, respectively. Although AL and VL in myopia patients were significantly longer than emmetropic and hyperopic eyes (p < 0.001), the ACD was not significantly different (p = 0.427). While the RLP is smaller in myopic eyes, lens thickness was increased in hyperopic eyes. PACG was significantly higher in elderly patients compared to PACS and PAC (p = 0.005). A total of 37 (13.8%) eyes were blind (vision worse than 3/60) and 19 of them (51.3%) were female patients.
CONCLUSION: A decrease in RLP is predictive of angle closure disease in myopic eyes, whereas increased lens thickness contributes to angle closure disease in hyperopic eyes.

Matched MeSH terms: Biometry/methods*

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