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Article: Sepsis and Its Impact on Outcomes in Elderly Patients Admitted to a Malaysian Intensive Care Unit.

Wan Muhd Shukeri, Wan Fadzlina / Mat Nor, Mohd Basri / Md Ralib, Azrina

The Malaysian journal of medical sciences : MJMS

2022 Volume 29, Issue 3, Page(s) 145–150

Abstract: Sepsis is an important cause of morbidity and mortality in elderly patients, but there is a scarcity of data on sepsis in this specific cohort. We performed this study to review the impact of sepsis on outcomes in elderly patients admitted to our local ... ...

Abstract	Sepsis is an important cause of morbidity and mortality in elderly patients, but there is a scarcity of data on sepsis in this specific cohort. We performed this study to review the impact of sepsis on outcomes in elderly patients admitted to our local intensive care unit (ICU). This was a secondary analysis of prospectively collected data of 159 consecutive adult patients with sepsis admitted to an ICU of a tertiary hospital in Malaysia over a three-year period. Of the 159 patients analysed, elderly patients constituted 18.9% of the cohort. Fifty percent of the older patients died within 30 days, compared to 24% of younger patients (
Language	English
Publishing date	2022-06-28
Publishing country	Malaysia
Document type	Journal Article
ZDB-ID	2197205-9
ISSN	2180-4303 ; 1394-195X
ISSN (online)	2180-4303
ISSN	1394-195X
DOI	10.21315/mjms2022.29.3.14
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Article: The Utility of the Creatinine Excretion to Production Ratio and the Plasma Creatinine and Cystatin C Based Kinetic Estimates of Glomerular Filtration Rates in Critically Ill Patients with Sepsis.

Md Ralib, Azrina / Ramly, Nur Fariza / Nanyan, Suhaila / Mat Nor, Mohd Basri

Indian journal of nephrology

2022 Volume 32, Issue 6, Page(s) 600–605

Abstract: Introduction: Creatinine kinetics denotes that under steady-state conditions, creatinine production (G) will equal creatinine excretion rate (E). The glomerular filtration (GFR) is impaired when excretion is less than production. The kinetic estimate of ...

Abstract	Introduction: Creatinine kinetics denotes that under steady-state conditions, creatinine production (G) will equal creatinine excretion rate (E). The glomerular filtration (GFR) is impaired when excretion is less than production. The kinetic estimate of GFR (keGFR) and E/G ratio were proposed as a more accurate estimate of GFR in acute settings with rapidly changing kidney function. We evaluated keGFR and E/G to diagnose AKI, predict recovery, death or dialysis. Methods: This is a prospective observational study of critically ill patients. Inclusion criteria were patients >18 years old with sepsis, defined as clinical infection with an increase in SOFA score >2, and plasma procalcitonin >0.5 ng/mL. Plasma creatinine and Cystatin C were measured on ICU admission and 4 h later, and their keGFR was calculated. Urine creatinine and urine output were measured over 4 h to calculate the E/G ratio. Results: A total of 70 patients were recruited, of which 49 (70%) had AKI. Of these, 33 recovered within 3 days, and 15 had a composite outcome of death or dialysis. Day 1 keGFR Conclusion: keGFR was strongly diagnostic of AKI. The E/G ratio predicted AKI recovery and a composite outcome of death and dialysis.
Language	English
Publishing date	2022-08-08
Publishing country	India
Document type	Journal Article
ZDB-ID	2134388-3
ISSN	1998-3662 ; 0971-4065
ISSN (online)	1998-3662
ISSN	0971-4065
DOI	10.4103/ijn.ijn_519_21
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Article ; Online: Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model.

Zainol, Nurhidayah Mohd / Damanhuri, Nor Salwa / Othman, Nor Azlan / Chiew, Yeong Shiong / Nor, Mohd Basri Mat / Muhammad, Zuraida / Chase, J Geoffrey

Computer methods and programs in biomedicine

2022 Volume 220, Page(s) 106835

Abstract: Background and objective: Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can ... ...

Abstract	Background and objective: Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model. Methods: Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data. Results and discussion: The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort. Conclusion: This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.
MeSH term(s)	Humans ; Incidence ; Respiration, Artificial ; Respiratory Distress Syndrome/therapy ; Respiratory Mechanics ; Ventilator-Induced Lung Injury
Language	English
Publishing date	2022-04-26
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2022.106835
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Article ; Online: Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach.

Ang, Christopher Yew Shuen / Chiew, Yeong Shiong / Wang, Xin / Ooi, Ean Hin / Nor, Mohd Basri Mat / Cove, Matthew E / Chase, J Geoffrey

Computer methods and programs in biomedicine

2023 Volume 240, Page(s) 107728

Abstract: Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic ... ...

Abstract	Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. Methods: A stochastic model was developed using respiratory elastance (E Results: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of E Conclusion: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
MeSH term(s)	Humans ; Respiration, Artificial/methods ; Retrospective Studies ; Computer Simulation ; Critical Care/methods ; Research Design
Language	English
Publishing date	2023-07-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2023.107728
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Article ; Online: Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach.

Ang, Christopher Yew Shuen / Chiew, Yeong Shiong / Wang, Xin / Mat Nor, Mohd Basri / Cove, Matthew E / Chase, J Geoffrey

Computers in biology and medicine

2022 Volume 151, Issue Pt A, Page(s) 106275

Abstract: Background and objective: Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe ... ...

Abstract	Background and objective: Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment. Methods: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (E Results: Clinical validation shows all three models captured more than 98% (median) of future E Conclusion: The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.
MeSH term(s)	Humans ; Respiration, Artificial/methods ; Positive-Pressure Respiration/methods ; Retrospective Studies ; Respiratory Mechanics ; Respiratory System
Language	English
Publishing date	2022-11-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.106275
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Article: Development and Validation of Creatinine-Based Estimates of the Glomerular Filtration Rate Equation from

Md Ralib, Azrina / Mohd Hanafiah, Farah Nadia / Abd Rashid, Iqbalmunawwir / Abd Rahim, Mohamad Shahrir / Dzaharudin, Fatimah / Mat Nor, Mohd Basri

International journal of nephrology

2021 Volume 2021, Page(s) 3465472

Abstract: Introduction: Accurate assessment of glomerular filtration rate (GFR) is very important for diagnostic and therapeutic intervention. Clinically, GFR is estimated from plasma creatinine using equations such as Cockcroft-Gault, Modification of Diet in ... ...

Abstract	Introduction: Accurate assessment of glomerular filtration rate (GFR) is very important for diagnostic and therapeutic intervention. Clinically, GFR is estimated from plasma creatinine using equations such as Cockcroft-Gault, Modification of Diet in Renal Disease, and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations. However, these were developed in the Western population. To the best of our knowledge, there was no equation that has been developed specifically in our population. Objectives: We developed a new equation based on the gold standard of Methods: This was a cross-sectional study using the existing record of patients who were referred for Results: Data of 187 patients were analysed from January 2016 to March 2021. Of these, 94 were randomised to the development cohort and 93 to the validation cohort. A new equation of eGFR was determined as 16.637 ∗ 0.9935 Conclusion: The new equation which was developed specifically using our local data population was the most accurate and precise, with less bias compared to the other equations. Further study validating this equation in the perioperative and intensive care patients is needed.
Language	English
Publishing date	2021-09-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2573904-9
ISSN	2090-2158 ; 2090-214X
ISSN (online)	2090-2158
ISSN	2090-214X
DOI	10.1155/2021/3465472
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Article ; Online: Stochastic integrated model-based protocol for volume-controlled ventilation setting.

Lee, Jay Wing Wai / Chiew, Yeong Shiong / Wang, Xin / Mat Nor, Mohd Basri / Chase, J Geoffrey / Desaive, Thomas

Biomedical engineering online

2022 Volume 21, Issue 1, Page(s) 13

Abstract: Background and objective: Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may ... ...

Abstract	Background and objective: Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. Methods: A stochastic model of E Results: From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. Conclusions: Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
MeSH term(s)	Humans ; Respiration, Artificial ; Respiratory System ; Retrospective Studies
Language	English
Publishing date	2022-02-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2084374-4
ISSN	1475-925X ; 1475-925X
ISSN (online)	1475-925X
ISSN	1475-925X
DOI	10.1186/s12938-022-00981-0
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Article: Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU.

Muhamad Sauki, Nur Sa'adah / Damanhuri, Nor Salwa / Othman, Nor Azlan / Chiew Meng, Belinda Chong / Chiew, Yeong Shiong / Mat Nor, Mohd Basri

Bioengineering (Basel, Switzerland)

2021 Volume 8, Issue 12

Abstract: Respiratory system modelling can assist clinicians in making clinical decisions during mechanical ventilation (MV) management in intensive care. However, there are some cases where the MV patients produce asynchronous breathing (asynchrony events) due to ...

Abstract	Respiratory system modelling can assist clinicians in making clinical decisions during mechanical ventilation (MV) management in intensive care. However, there are some cases where the MV patients produce asynchronous breathing (asynchrony events) due to the spontaneous breathing (SB) effort even though they are fully sedated. Currently, most of the developed models are only suitable for fully sedated patients, which means they cannot be implemented for patients who produce asynchrony in their breathing. This leads to an incorrect measurement of the actual underlying mechanics in these patients. As a result, there is a need to develop a model that can detect asynchrony in real-time and at the bedside throughout the ventilated days. This paper demonstrates the asynchronous event detection of MV patients in the ICU of a hospital by applying a developed extended time-varying elastance model. Data from 10 mechanically ventilated respiratory failure patients admitted at the International Islamic University Malaysia (IIUM) Hospital were collected. The results showed that the model-based technique precisely detected asynchrony events (AEs) throughout the ventilation days. The patients showed an increase in AEs during the ventilation period within the same ventilation mode. SIMV mode produced much higher asynchrony compared to SPONT mode (
Language	English
Publishing date	2021-12-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering8120222
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Article ; Online: CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring.

Arn Ng, Qing / Yew Shuen Ang, Christopher / Shiong Chiew, Yeong / Wang, Xin / Pin Tan, Chee / Basri Mat Nor, Mohd / Salwa Damanhuri, Nor / Geoffrey Chase, J

HardwareX

2022 Volume 12, Page(s) e00358

Abstract: ... information. However, existing systems do not provide full access to this information nor allow for real-time ...

Abstract	Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.
Language	English
Publishing date	2022-09-06
Publishing country	England
Document type	Journal Article
ISSN	2468-0672
ISSN (online)	2468-0672
DOI	10.1016/j.ohx.2022.e00358
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Article ; Online: Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients.

Lee, Jay Wing Wai / Chiew, Yeong Shiong / Wang, Xin / Tan, Chee Pin / Mat Nor, Mohd Basri / Damanhuri, Nor Salwa / Chase, J Geoffrey

Annals of biomedical engineering

2021 Volume 49, Issue 12, Page(s) 3280–3295

Abstract: While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to ... ...

Abstract	While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, E
MeSH term(s)	Adult ; Aged ; Elasticity ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Prospective Studies ; Respiration, Artificial/methods ; Respiratory Insufficiency/physiopathology ; Respiratory Insufficiency/therapy ; Respiratory Mechanics/physiology ; Stochastic Processes
Language	English
Publishing date	2021-08-25
Publishing country	United States
Document type	Journal Article ; Observational Study
ZDB-ID	185984-5
ISSN	1573-9686 ; 0191-5649 ; 0090-6964
ISSN (online)	1573-9686
ISSN	0191-5649 ; 0090-6964
DOI	10.1007/s10439-021-02854-4
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