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  1. Ralib AM, Nanyan S, Mat Nor MB
    Indian J Crit Care Med, 2017 Jan;21(1):23-29.
    PMID: 28197047 DOI: 10.4103/0972-5229.198322
    About 50% of patients admitted to the Intensive Care Unit have systemic inflammatory response syndrome (SIRS), and about 10%-20% of them died. Early risk stratification is important to reduce mortality. Plasma neutrophil gelatinase-associated lipocalin (NGAL) is increased by inflammation and infection. Its ability to predict mortality in SIRS patients is of interest. We evaluated the ability of serial measurement of NGAL for the prediction of mortality in critically ill patients with SIRS.
  2. Ralib AM, Pickering JW, Shaw GM, Than MP, George PM, Endre ZH
    Crit Care, 2014;18(6):601.
    PMID: 25366893 DOI: 10.1186/s13054-014-0601-2
    INTRODUCTION: Acute Kidney Injury (AKI) biomarker utility depends on sample timing after the onset of renal injury. We compared biomarker performance on arrival in the emergency department (ED) with subsequent performance in the intensive care unit (ICU).
    METHODS: Urinary and plasma Neutrophil Gelatinase-Associated Lipocalin (NGAL), and urinary Cystatin C (CysC), alkaline phosphatase, γ-Glutamyl Transpeptidase (GGT), α- and π-Glutathione S-Transferase (GST), and albumin were measured on ED presentation, and at 0, 4, 8, and 16 hours, and days 2, 4 and 7 in the ICU in patients after cardiac arrest, sustained or profound hypotension or ruptured abdominal aortic aneurysm. AKI was defined as plasma creatinine increase ≥ 26.5 μmol/l within 48 hours or ≥ 50% within 7 days.
    RESULTS: n total, 45 of 77 patients developed AKI. Most AKI patients had elevated urinary NGAL, and plasma NGAL and CysC in the period 6 to 24 hours post presentation. Biomarker performance in the ICU was similar or better than when measured earlier in the ED. Plasma NGAL diagnosed AKI at all sampling times, urinary NGAL, plasma and urinary CysC up to 48 hours, GGT 4 to 12 hours, and π-GST 8 to 12 hours post insult. Thirty-one patients died or required dialysis. Peak 24-hour urinary NGAL and albumin independently predicted 30-day mortality and dialysis; odds ratios 2.87 (1.32 to 6.26), and 2.72 (1.14 to 6.48), respectively. Urinary NGAL improved risk prediction by 11% (IDI event of 0.06 (0.002 to 0.19) and IDI non-event of 0.04 (0.002 to 0.12)).
    CONCLUSION: Early measurement in the ED has utility, but not better AKI diagnostic performance than later ICU measurement. Plasma NGAL diagnosed AKI at all time points. Urinary NGAL best predicted mortality or dialysis compared to other biomarkers.
    TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12610001012066. Registered 12 February 2010.
  3. Ralib AM, Nanyan S, Ramly NF, Har LC, Cheng TC, Mat Nor MB
    Indian J Crit Care Med, 2018 Dec;22(12):831-835.
    PMID: 30662220 DOI: 10.4103/ijccm.IJCCM_193_18
    Introduction: Acute kidney injury (AKI) is common in the intensive care unit (ICU) with a high risk of morbidity and mortality. The high incidence of AKI in our population may be attributed to sepsis. We investigated the incidence, risk factors, and outcome of AKI in four tertiary Malaysian ICUs. We also evaluated its association with sepsis.

    Materials and Methods: This retrospective cohort study extracted de-identified data from the Malaysian Registry of Intensive Care in four Malaysian tertiary ICUs between January 2010 and December 2014. The study was registered under the NMRR and approved by the ethics committee. AKI was defined as twice the baseline creatinine or urine output <0.5 ml/kg/h for 12 h.

    Results: Of 26,663 patients, 24.2% had AKI within 24 h of admission. Patients with AKI were older and had higher severity of illness compared to those without AKI. AKI patients had a longer duration of mechanical ventilation, length of ICU, and hospital stay. Age, Simplified Acute Physiological II Score, and the presence of sepsis and preexisting hypertension, chronic cardiovascular disease independently associated with AKI. About 32.3% had sepsis. Patients with both AKI and sepsis had the highest risk of mortality (relative risk 3.43 [3.34-3.53]).

    Conclusions: AKI is common in our ICU, with higher morbidity and mortality. Independent risk factors of AKI include age, the severity of illness, sepsis and preexisting hypertension, and chronic cardiovascular disease. AKI independently contributes to mortality. The presence of AKI and sepsis increased the risk of mortality by three times.

  4. Muhd Shukeri WFW, Mat-Nor MB, Jamaludin UK, Suhaimi F, Abd Razak NN, Ralib AM
    Indian J Crit Care Med, 2018 Jun;22(6):402-407.
    PMID: 29962739 DOI: 10.4103/ijccm.IJCCM_92_18
    Background and Aims: Currently, there is a lack of real-time metric with high sensitivity and specificity to diagnose sepsis. Insulin sensitivity (SI) may be determined in real-time using mathematical glucose-insulin models; however, its effectiveness as a diagnostic test of sepsis is unknown. Our aims were to determine the levels and diagnostic value of model-based SI for identification of sepsis in critically ill patients.

    Materials and Methods: In this retrospective, cohort study, we analyzed SI levels in septic (n = 18) and nonseptic (n = 20) patients at 1 (baseline), 4, 8, 12, 16, 20, and 24 h of their Intensive Care Unit admission. Patients with diabetes mellitus Type I or Type II were excluded from the study. The SI levels were derived by fitting the blood glucose levels, insulin infusion and glucose input rates into the Intensive Control of Insulin-Nutrition-Glucose model.

    Results: The median SI levels were significantly lower in the sepsis than in the nonsepsis at all follow-up time points. The areas under the receiver operating characteristic curve of the model-based SI at baseline for discriminating sepsis from nonsepsis was 0.814 (95% confidence interval, 0.675-0.953). The optimal cutoff point of the SI test was 1.573 × 10-4 L/mu/min. At this cutoff point, the sensitivity was 77.8%, specificity was 75%, positive predictive value was 73.7%, and negative predictive value was 78.9%.

    Conclusions: Model-based SI ruled in and ruled out sepsis with fairly high sensitivity and specificity in our critically ill nondiabetic patients. These findings can be used as a foundation for further, prospective investigation in this area.

  5. Arunachalam GR, Chiew YS, Tan CP, Ralib AM, Nor MBM
    Comput Methods Programs Biomed, 2020 Jan;183:105103.
    PMID: 31606559 DOI: 10.1016/j.cmpb.2019.105103
    BACKGROUND AND OBJECTIVE: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient's spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate respiratory mechanics in the presence of patient effort.

    METHODS: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.

    RESULTS AND DISCUSSION: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data.

    CONCLUSION: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.

  6. Shukeri WFWM, Ralib AM, Abdulah NZ, Mat-Nor MB
    J Crit Care, 2018 Feb;43:163-168.
    PMID: 28903084 DOI: 10.1016/j.jcrc.2017.09.009
    PURPOSE: To derive a prediction equation for 30-day mortality in sepsis using a multi-marker approach and compare its performance to the Sequential Organ Failure Assessment (SOFA) score.

    METHODS: This study included 159 septic patients admitted to an intensive care unit. Leukocytes count, procalcitonin (PCT), interleukin-6 (IL-6), and paraoxonase (PON) and arylesterase (ARE) activities of PON-1 were assayed from blood obtained on ICU presentation. Logistic regression was used to derive sepsis mortality score (SMS), a prediction equation describing the relationship between biomarkers and 30-day mortality.

    RESULTS: The 30-day mortality rate was 28.9%. The SMS was [еlogit(p)/(1+еlogit(p))]×100; logit(p)=0.74+(0.004×PCT)+(0.001×IL-6)-(0.025×ARE)-(0.059×leukocytes count). The SMC had higher area under the receiver operating characteristic curve (95% Cl) than SOFA score [0.814 (0.736-0.892) vs. 0.767 (0.677-0.857)], but is not statistically significant. When the SMS was added to the SOFA score, prediction of 30-day mortality improved compared to SOFA score used alone [0.845 (0.777-0.899), p=0.022].

    CONCLUSIONS: A sepsis mortality score using baseline leukocytes count, PCT, IL-6 and ARE was derived, which predicted 30-day mortality with very good performance and added significant prognostic information to SOFA score.

  7. Chiew YS, Tan CP, Chase JG, Chiew YW, Desaive T, Ralib AM, et al.
    Comput Methods Programs Biomed, 2018 Apr;157:217-224.
    PMID: 29477430 DOI: 10.1016/j.cmpb.2018.02.007
    BACKGROUND AND OBJECTIVE: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation.

    METHODS: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn).

    RESULTS AND DISCUSSION: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%.

    CONCLUSION: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.

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