Examining the impact of a 9-component bundle and the INICC multidimensional approach on catheter-associated urinary tract infection rates in 32 countries across Asia, Eastern Europe, Latin America, and the Middle East.

BACKGROUND: Catheter-Associated Urinary Tract Infections (CAUTIs) frequently occur in the intensive care unit (ICU) and are correlated with a significant burden. METHODS: We implemented a strategy involving a 9-element bundle, education, surveillance of CAUTI rates and clinical outcomes, monitoring compliance with bundle components, feedback of CAUTI rates and performance feedback. This was executed in 299 ICUs across 32 low- and middle-income countries. The dependent variable was CAUTI per 1,000 UC days, assessed at baseline and throughout the intervention, in the second month, third month, 4 to 15 months, 16 to 27 months, and 28 to 39 months. Comparisons were made using a 2-sample t test, and the exposure-outcome relationship was explored using a generalized linear mixed model with a Poisson distribution. RESULTS: Over the course of 978,364 patient days, 150,258 patients utilized 652,053 UC-days. The rates of CAUTI per 1,000 UC days were measured. The rates decreased from 14.89 during the baseline period to 5.51 in the second month (risk ratio [RR] = 0.37; 95% confidence interval [CI] = 0.34-0.39; P < .001), 3.79 in the third month (RR = 0.25; 95% CI = 0.23-0.28; P < .001), 2.98 in the 4 to 15 months (RR = 0.21; 95% CI = 0.18-0.22; P < .001), 1.86 in the 16 to 27 months (RR = 0.12; 95% CI = 0.11-0.14; P < .001), and 1.71 in the 28 to 39 months (RR = 0.11; 95% CI = 0.09-0.13; P < .001). CONCLUSIONS: Our intervention, without substantial costs or additional staffing, achieved an 89% reduction in CAUTI incidence in ICUs across 32 countries, demonstrating feasibility in ICUs of low- and middle-income countries.

International Nosocomial Infection Control Consortium (INICC) report of health care associated infections, data summary of 45 countries for 2015 to 2020, adult and pediatric units, device-associated module.

BACKGROUND: Reporting on the International Nosocomial Infection Control Consortium study results from 2015 to 2020, conducted in 630 intensive care units across 123 cities in 45 countries spanning Africa, Asia, Eastern Europe, Latin America, and the Middle East. METHODS: Prospective intensive care unit patient data collected via International Nosocomial Infection Control Consortium Surveillance Online System. Centers for Disease Control and Prevention/National Health Care Safety Network definitions applied for device-associated health care-associated infections (DA-HAI). RESULTS: We gathered data from 204,770 patients, 1,480,620 patient days, 936,976 central line (CL)-days, 637,850 mechanical ventilators (MV)-days, and 1,005,589 urinary catheter (UC)-days. Our results showed 4,270 CL-associated bloodstream infections, 7,635 ventilator-associated pneumonia, and 3,005 UC-associated urinary tract infections. The combined rates of DA-HAIs were 7.28%, and 10.07 DA-HAIs per 1,000 patient days. CL-associated bloodstream infections occurred at 4.55 per 1,000 CL-days, ventilator-associated pneumonias at 11.96 per 1,000 MV-days, and UC-associated urinary tract infections at 2.91 per 1,000 UC days. In terms of resistance, Pseudomonas aeruginosa showed 50.73% resistance to imipenem, 44.99% to ceftazidime, 37.95% to ciprofloxacin, and 34.05% to amikacin. Meanwhile, Klebsiella spp had resistance rates of 48.29% to imipenem, 72.03% to ceftazidime, 61.78% to ciprofloxacin, and 40.32% to amikacin. Coagulase-negative Staphylococci and Staphylococcus aureus displayed oxacillin resistance in 81.33% and 53.83% of cases, respectively. CONCLUSIONS: The high rates of DA-HAI and bacterial resistance emphasize the ongoing need for continued efforts to control them.

Critical care bed capacity in Asian countries and regions before and during the COVID-19 pandemic: an observational study.

Background: The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of critical care. The aim of the current study was to compare the number of adult critical care beds in relation to population size in Asian countries and regions before (2017) and during (2022) the pandemic. Methods: This observational study collected data closest to 2022 on critical care beds (intensive care units and intermediate care units) in 12 middle-income and 7 high-income economies (using the 2022-2023 World Bank classification), through a mix of methods including government sources, national critical care societies, personal contacts, and data extrapolation. Data were compared with a prior study from 2017 of the same countries and regions. Findings: The cumulative number of critical care beds per 100,000 population increased from 3.0 in 2017 to 9.4 in 2022 (p = 0.003). The median figure for middle-income economies increased from 2.6 (interquartile range [IQR] 1.7-7.8) to 6.6 (IQR 2.2-13.3), and that for high-income economies increased from 11.4 (IQR 7.3-22.8) to 13.9 (IQR 10.7-21.7). Only 3 countries did not see a rise in bed capacity. Where data were available in 2022, 10.9% of critical care beds were in single rooms (median 5.0% in middle-income and 20.3% in high-income economies), and 5.3% had negative pressure (median 0.7% in middle-income and 18.5% in high-income economies). Interpretation: Critical care bed capacity in the studied Asian countries and regions increased close to three-fold from 2017 to 2022. Much of this increase was attributed to middle-income economies, but substantial heterogeneity exists. Funding: None.

A protocol for an international, multicentre pharmacokinetic study for Screening Antifungal Exposure in Intensive Care Units: The SAFE-ICU study.

Objective: To describe whether contemporary dosing of antifungal drugs achieves therapeutic exposures in critically ill patients that are associated with optimal outcomes. Adequate antifungal therapy is a key determinant of survival of critically ill patients with fungal infections. Critical illness can alter an antifungal agents' pharmacokinetics, increasing the risk of inappropriate antifungal exposure that may lead to treatment failure and/or toxicity. Design setting and participants: This international, multicentre, observational pharmacokinetic study will comprise adult critically ill patients prescribed antifungal agents including fluconazole, voriconazole, posaconazole, isavuconazole, caspofungin, micafungin, anidulafungin, and amphotericin B for the treatment or prophylaxis of invasive fungal disease. A minimum of 12 patients are targeted for enrolment for each antifungal agent, across 12 countries and 30 intensive care units to perform descriptive pharmacokinetics. Pharmacokinetic sampling will occur during two dosing intervals (occasions): firstly, between days 1 and 3, and secondly, between days 4 and 7 of the antifungal course, collecting three samples per occasion. Patients' demographic and clinical data will be collected. Main outcome measures: The primary endpoint of the study is attainment of pharmacokinetic/pharmacodynamic target exposures that are associated with optimal efficacy. Thirty-day mortality will also be measured. Results and conclusions: This study will describe whether contemporary antifungal drug dosing achieves drug exposures associated with optimal outcomes. Data will also be used for the development of antifungal dosing algorithms for critically ill patients. Optimised drug dosing should be considered a priority for improving clinical outcomes for critically ill patients with fungal infections.

Protocol and statistical analysis plan for the mega randomised registry trial comparing conservative vs. liberal oxygenation targets in adults with nonhypoxic ischaemic acute brain injuries and conditions in the intensive care unit (Mega-ROX Brains).

Background: The effect of conservative vs. liberal oxygen therapy on 90-day in-hospital mortality in adults who have nonhypoxic ischaemic encephalopathy acute brain injuries and conditions and are receiving invasive mechanical ventilation in the intensive care unit (ICU) is uncertain. Objective: The objective of this study was to summarise the protocol and statistical analysis plan for the Mega-ROX Brains trial. Design setting and participants: Mega-ROX Brains is an international randomised clinical trial, which will be conducted within an overarching 40,000-participant, registry-embedded clinical trial comparing conservative and liberal ICU oxygen therapy regimens. We expect to enrol between 7500 and 9500 participants with nonhypoxic ischaemic encephalopathy acute brain injuries and conditions who are receiving unplanned invasive mechanical ventilation in the ICU. Main outcome measures: The primary outcome is in-hospital all-cause mortality up to 90 d from the date of randomisation. Secondary outcomes include duration of survival, duration of mechanical ventilation, ICU length of stay, hospital length of stay, and the proportion of participants discharged home. Results and conclusions: Mega-ROX Brains will compare the effect of conservative vs. liberal oxygen therapy regimens on 90-day in-hospital mortality in adults in the ICU with acute brain injuries and conditions. The protocol and planned analyses are reported here to mitigate analysis bias. Trial Registration: Australian and New Zealand Clinical Trials Registry (ACTRN 12620000391976).

Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach.

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 (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves. RESULTS: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range. 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.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients.

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome. Funding: Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford's COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.

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

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 (P = 0.005). On multivariate analysis, old age was found to be independently predictive of 30-day mortality with an adjusted odds ratio (OR) of 2.5 (95% confidence interval [CI]: 1.05, 6.01) compared to younger patients (P = 0.021). In a Kaplan-Meier analysis, survival probability was significantly lower in patients of an older age compared to younger patients (P = 0.015). In conclusion, mortality from sepsis is considerably higher in elderly patients, with age as an independent risk factor for mortality.

Epidemiology, Management, and Outcomes of Sepsis in ICUs among Countries of Differing National Wealth across Asia.

Rationale: Directly comparative data on sepsis epidemiology and sepsis bundle implementation in countries of differing national wealth remain sparse. Objectives: To evaluate across countries/regions of differing income status in Asia 1) the prevalence, causes, and outcomes of sepsis as a reason for ICU admission and 2) sepsis bundle (antibiotic administration, blood culture, and lactate measurement) compliance and its association with hospital mortality. Methods: A prospective point prevalence study was conducted among 386 adult ICUs from 22 Asian countries/regions. Adult ICU participants admitted for sepsis on four separate days (representing the seasons of 2019) were recruited. Measurements and Main Results: The overall prevalence of sepsis in ICUs was 22.4% (20.9%, 24.5%, and 21.3% in low-income countries/regions [LICs]/lower middle-income countries/regions [LMICs], upper middle-income countries/regions, and high-income countries/regions [HICs], respectively; P < 0.001). Patients were younger and had lower severity of illness in LICs/LMICs. Hospital mortality was 32.6% and marginally significantly higher in LICs/LMICs than HICs on multivariable generalized mixed model analysis (adjusted odds ratio, 1.84; 95% confidence interval, 1.00-3.37; P = 0.049). Sepsis bundle compliance was 21.5% at 1 hour (26.0%, 22.1%, and 16.2% in LICs/LMICs, upper middle-income countries/regions, and HICs, respectively; P < 0.001) and 36.6% at 3 hours (39.3%, 32.8%, and 38.5%, respectively; P = 0.001). Delaying antibiotic administration beyond 3 hours was the only element independently associated with increased mortality (adjusted odds ratio, 2.53; 95% confidence interval, 2.07-3.08; P < 0.001). Conclusions: Sepsis is a common cause of admission to Asian ICUs. Mortality remains high and is higher in LICs/LMICs after controlling for confounders. Sepsis bundle compliance remains low. Delaying antibiotic administration beyond 3 hours from diagnosis is associated with increased mortality. Clinical trial registered with www.ctri.nic.in (CTRI/2019/01/016898).

Stochastic integrated model-based protocol for volume-controlled ventilation setting.

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 Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each. 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.

Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses. METHODS: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume. RESULTS: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained. CONCLUSIONS: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

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.

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 keGFRCr and keGFRCysC discriminated AKI from non-AKI with AUCs of 0.85 (95% Confidence interval: 0.74-0.96), and 0.86 (0.76-0.97), respectively. The E/G ratio predicted AKI recovery (AUC: 0.81 (0.69-0.97)). The keGFRs were not predictive of death or dialysis, whereas E/G was predictive (AUC: 0.76 (0.63-0.89). Conclusion: keGFR was strongly diagnostic of AKI. The E/G ratio predicted AKI recovery and a composite outcome of death and dialysis.

The Combined Use of Interleukin-6 with Serum Albumin for Mortality Prediction in Critically Ill Elderly Patients: The Interleukin-6-to-albumin Ratio.

Background: The association between interleukin-6 (IL-6) and serum albumin (ALB) with mortality in critically ill elderly patients, either as stand-alone biomarkers or in combination, has been scarcely reported. We, therefore, aimed to investigate the prognostic value of the IL-6-to-albumin ratio in this special population. Patients and methods: This was a cross-sectional study conducted in the mixed intensive care unit (ICU) of two university-affiliated hospitals in Malaysia. Consecutive elderly patients (aged above or equal to 60 years) admitted to the ICU, who underwent simultaneous measurement of plasma IL-6 and serum ALB, were recruited. The prognostic value of the IL-6-to-albumin ratio was assessed by analysis of the receiver-operating characteristic (ROC) curve. Results: A total of 112 critically ill elderly patients were recruited. The outcome of all-cause ICU mortality was 22.3%. The calculated IL-6-to-albumin ratio was significantly higher in the non-survivors compared to the survivors {14.1 [interquartile range (IQR), 6.5-26.7] vs 2.5 [(IQR, 0.6-9.2) pg/mL, p <0.001]}. The area under the curve (AUC) of IL-6-to-albumin ratio for discrimination of ICU mortality was 0.766 [95% confidence interval (CI), 0.667-0.865, p <0.001] which was slightly higher than that of IL-6 and albumin alone. The ideal cut-off value of the IL-6-to-albumin ratio was above 5.7 with a sensitivity of 80.0% and specificity of 64.4%. After adjusting for severity of illness, the IL-6-to-albumin ratio remained as an independent predictor of ICU mortality with an adjusted odd ratio of 0.975 (95% CI, 0.952-0.999, p = 0.039). Conclusion: The IL-6-to-albumin ratio offers a slight improvement in mortality prediction than either of its constituent individual biomarkers and as such, it may be a potential tool to aid in the prognostication of critically ill elderly patients although this requires further validation in a larger prospective study. How to cite this article: Lim KY, Shukeri WFWM, Hassan WMNW, Mat-Nor MB, Hanafi MH. The Combined Use of Interleukin-6 with Serum Albumin for Mortality Prediction in Critically Ill Elderly Patients: The Interleukin-6-to-albumin Ratio. Indian J Crit Care Med 2022;26(10):1126-1130.

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements.

BACKGROUND: Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment. METHODS: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference. RESULTS: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)-5.6(Age) - 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late]. CONCLUSIONS: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases. TRIAL REGISTRATION: ClinicalTrials.gov NCT03319329.

Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU.

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 (p < 0.05). The link between AEs and the lung elastance (AUC Edrs) was also investigated. It was found that when the AEs increased, the AUC Edrs decreased and vice versa based on the results obtained in this research. The information of AEs and AUC Edrs provides the true underlying lung mechanics of the MV patients. Hence, this model-based method is capable of detecting the AEs in fully sedated MV patients and providing information that can potentially guide clinicians in selecting the optimal ventilation mode of MV, allowing for precise monitoring of respiratory mechanics in MV patients.

The story of critical care in Asia: a narrative review.

BACKGROUND: Asia has more critically ill people than any other part of our planet. The aim of this article is to review the development of critical care as a specialty, critical care societies and education and research, the epidemiology of critical illness as well as epidemics and pandemics, accessibility and cost and quality of critical care, culture and end-of-life care, and future directions for critical care in Asia. MAIN BODY: Although the first Asian intensive care units (ICUs) surfaced in the 1960s and the 1970s and specialisation started in the 1990s, multiple challenges still exist, including the lack of intensivists, critical care nurses, and respiratory therapists in many countries. This is aggravated by the brain drain of skilled ICU staff to high-income countries. Critical care societies have been integral to the development of the discipline and have increasingly contributed to critical care education, although critical care research is only just starting to take off through collaboration across groups. Sepsis, increasingly aggravated by multidrug resistance, contributes to a significant burden of critical illness, while epidemics and pandemics continue to haunt the continent intermittently. In particular, the coronavirus disease 2019 (COVID-19) has highlighted the central role of critical care in pandemic response. Accessibility to critical care is affected by lack of ICU beds and high costs, and quality of critical care is affected by limited capability for investigations and treatment in low- and middle-income countries. Meanwhile, there are clear cultural differences across countries, with considerable variations in end-of-life care. Demand for critical care will rise across the continent due to ageing populations and rising comorbidity burdens. Even as countries respond by increasing critical care capacity, the critical care community must continue to focus on training for ICU healthcare workers, processes anchored on evidence-based medicine, technology guided by feasibility and impact, research applicable to Asian and local settings, and rallying of governments for support for the specialty. CONCLUSIONS: Critical care in Asia has progressed through the years, but multiple challenges remain. These challenges should be addressed through a collaborative approach across disciplines, ICUs, hospitals, societies, governments, and countries.

Development and Validation of Creatinine-Based Estimates of the Glomerular Filtration Rate Equation from 99mTc-DTPA Imaging in the Malaysian Setting.

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 99mTc-DTPA imaging measured GFR. We then performed an internal validation by comparing the bias, precision, and accuracy of the new equation and the other equations with the gold standard of 99mTc-DTPA imaging measured GFR. METHODS: This was a cross-sectional study using the existing record of patients who were referred for 99mTc-DTPA imaging at the Nuclear Medicine Centre, International Islamic University Malaysia. As this is a retrospective study utilising routinely collected data from the existing pool of data, the ethical committee has waived the need for informed consent. 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 ∗ 0.9935Age ∗ (SCr/23.473)-0.45159. In the validation cohort, both CKD-EPI and the new equation had the highest correlation to 99mTc-DTPA with a correlation coefficient of 0.81 (p < 0.0001). However, the new equation had the least bias and was the most precise (mean bias of -3.58 ± 12.01) and accurate (P30 of 64.5% and P50 of 84.9%) compared to the other equations. 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.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients.

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, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.