Effects of 24-hour urine-output trajectories on the risk of acute kidney injury in critically ill patients with cirrhosis: a retrospective cohort analysis

BACKGROUND

Acute kidney injury (AKI) is one of the most common complications for critically ill patients with cirrhosis, but it has remained unclear whether urine output fluctuations are associated with the risk of AKI in such patients. Thus, we explored the influence of 24-h urine-output trajectory on AKI in patients with cirrhosis through latent category trajectory modeling.

MATERIALS AND METHODS

This retrospective cohort study examined patients with cirrhosis using the MIMIC-IV database. Changes in the trajectories of urine output within 24 h after admission to the intensive care unit (ICU) were categorized using latent category trajectory modeling. The outcome examined was the occurrence of AKI during ICU hospitalization. The risk of AKI in patients with different trajectory classes was explored using the cumulative incidence function (CIF) and the Fine-Gray model with the sub-distribution hazard ratio (SHR) and the 95% confidence interval (CI) as size effects.

RESULTS

The study included 3,562 critically ill patients with cirrhosis, of which 2,467 (69.26%) developed AKI during ICU hospitalization. The 24-h urine-output trajectories were split into five classes (Classes 1-5). The CIF curves demonstrated that patients with continuously low urine output (Class 2), a rapid decline in urine output after initially high levels (Class 3), and urine output that decreased slowly and then stabilized at a lower level (Class 4) were at higher risk for AKI than those with consistently moderate urine output (Class 1). After fully adjusting for various confounders, Classes 2, 3, and 4 were associated with a higher risk of AKI compared with Class 1, and the respective SHRs (95% CIs) were 2.56 (1.87-3.51), 1.86 (1.34-2.59), and 1.83 1.29-2.59).

CONCLUSIONS

The 24-h urine-output trajectory is significantly associated with the risk of AKI in critically ill patients with cirrhosis. More attention should be paid to the dynamic nature of urine-output changes over time, which may help guide early intervention and improve patients' prognoses.

Tenecteplase versus alteplase for the treatment of acute ischemic stroke: a meta-analysis of randomized controlled trials

OBJECTIVES

Tenecteplase, a modified variant of alteplase with greater fibrin specificity and longer plasma half-life, may have better efficacy and safety than alteplase in patients with acute ischemic stroke (AIS). We aimed to compare the benefits and risks of tenecteplase versus alteplase in the treatment of AIS.

METHODS

Electronic databases were searched up to 10 February 2023 for randomized controlled trials evaluating the effect of tenecteplase versus alteplase in the treatment of AIS. The primary outcome was functional outcome at 90 days, and secondary outcomes including the symptomatic intracranial haemorrhage (SICH), and major neurological improvement. Subgroup analysis was performed based on the different dosage of tenecteplase.

RESULTS

Ten studies with a total of 5123 patients were analysed in this meta-analysis. Overall, no significant difference between tenecteplase and alteplase was observed for functional outcome at 90 days (excellent: OR 1.08, 95%CI 0.93-1.26, I2 = 26%; good: OR 1.04, 95%CI 0.83-1.30, I2 = 56%; poor: OR 0.95, 95%CI 0.75-1.21, I2 = 31%), SICH (OR 1.12, 95%CI 0.79-1.59, I2 = 0%), and early major neurological improvement (OR 1.26, 95%CI 0.80-1.96, I2 = 65%). The subgroup analysis suggested that the 0.25 mg/kg dose of tenecteplase had potentially greater efficacy and lower symptomatic intracerebral haemorrhage risk compared with 0.25 mg/kg dose tenecteplase.

CONCLUSIONS

Among AIS patients, there was no significant difference on clinical outcomes between tenecteplase and alteplase. Subgroup analysis demonstrated that 0.25 mg/kg doses of tenecteplase were more beneficial than 0.4 mg/kg doses of tenecteplase. Further studies are required to identify the optimal dosage of tenecteplase.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine

BACKGROUND

The nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks.

PURPOSE

This study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment.

METHODS

Failure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine.

FINDINGS

Results prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data).

CONCLUSIONS

Despite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

Enteral Sedation in Patients Requiring Mechanical Ventilation During an Intravenous Analgesic and Sedative Shortage

Background: There is a paucity of data evaluating the use of enteral sedation in mechanical ventilation. A sedative shortage resulted in the use of this approach. Purpose: To evaluate the feasibility of using enteral sedatives to decrease intravenous (IV) analgesia and sedative requirements. Materials/Methods: This single-center, retrospective, observational study compared two groups of patients admitted to the ICU who were mechanically ventilated. One group received a combination of enteral and IV sedatives and the second group received IV monotherapy. Linear mixed model (LMM) analyses were performed to assess the impact of enteral sedatives on IV fentanyl equivalents, IV midazolam equivalents, and propofol. Mann-Whitney U tests were performed on percent of days at goal for Richmond Agitation and Sedation Scale (RASS) and critical care pain observation tool (CPOT) scores. Results: One hundred and four patients were included. The average cohort age was 62 years and 58.7% were male. The median length of mechanical ventilation was 7.1 days and the median length of stay was 11.9 days. The LMM estimated that enteral sedatives reduced IV fentanyl equivalents received per patient by an average of 305.6 mcg/day (P = .04), although did not significantly decrease midazolam equivalents or propofol. There was no statistically significant difference in CPOT scores (P = .57 and P = .46 respectively), however RASS scores in the enteral sedation group were more often at goal (P = .03); oversedation occurred more in the non-enteral sedation group (P = .018). Conclusion: Enteral sedation may be a possible way to decrease IV analgesia requirements during periods of shortage.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Semiconducting polymer dots based l-lactate sensor by enzymatic cascade reaction system

BACKGROUND

l-lactate detection is important for not only assessing exercise intensity, optimizing training regimens, and identifying the lactate threshold in athletes, but also for diagnosing conditions like L-lactateosis, monitoring tissue hypoxia, and guiding critical care decisions. Moreover, l-lactate has been utilized as a biomarker to represent the state of human health. However, the sensitivity of the present l-lactate detection technique is inadequate.

RESULTS

Here, we reported a sensitive ratiometric fluorescent probe for l-lactate detection based on platinum octaethylporphyrin (PtOEP) doped semiconducting polymer dots (Pdots-Pt) with enzymatic cascade reaction. With the help of an enzyme cascade reaction, the l-lactate was continuously oxidized to pyruvic and then reduced back to l-lactate for the next cycle. During this process, oxygen and NADH were continuously consumed, which increased the red fluorescence of Pdots-Pt that responded to the changes of oxygen concentration and decreased the blue fluorescence of NADH at the same time. By comparing the fluorescence intensities at these two different wavelengths, the concentration of l-lactate was accurately measured. With the optimal conditions, the probes showed two linear detection ranges from 0.5 nM to 5.0 μM and 5.0 μM-50.0 μM for l-lactate detection. The limit of detection was calculated to be 0.18 nM by 3σ/slope method. Finally, the method shows good detection performance of l-lactate in both bovine serum and artificial serum samples, indicating its potential usage for the selective analysis of l-lactate for health monitoring and disease diagnosis.

SIGNIFICANCE

The successful application of the sensing system in the complex biological sample (bovine serum and artificial serum samples) demonstrated that this method could be used for sensitive l-lactate detection in practical clinical applications. This detection system provided an extremely low detection limit, which was several orders of magnitude lower than methods proposed in other literatures.

Association of Mechanical Energy and Power with Postoperative Pulmonary Complications in Lung Resection Surgery: A Post Hoc Analysis of Randomized Clinical Trial Data

BACKGROUND

Mechanical power (MP), the rate of mechanical energy (ME) delivery, is a recently introduced unifying ventilator parameter consisting of tidal volume, airway pressures, and respiratory rates, which predicts pulmonary complications in several clinical contexts. However, ME has not been previously studied in the perioperative context, and neither parameter has been studied in the context of thoracic surgery utilizing one-lung ventilation.

METHODS

The relationships between ME variables and postoperative pulmonary complications were evaluated in this post hoc analysis of data from a multicenter randomized clinical trial of lung resection surgery conducted between 2020 and 2021 (n = 1,170). Time-weighted average MP and ME (the area under the MP time curve) were obtained for individual patients. The primary analysis was the association of time-weighted average MP and ME with pulmonary complications within 7 postoperative days. Multivariable logistic regression was performed to examine the relationships between energy variables and the primary outcome.

RESULTS

In 1,055 patients analyzed, pulmonary complications occurred in 41% (431 of 1,055). The median (interquartile ranges) ME and time-weighted average MP in patients who developed postoperative pulmonary complications versus those who did not were 1,146 (811 to 1,530) J versus 924 (730 to 1,240) J (P < 0.001), and 6.9 (5.5 to 8.7) J/min versus 6.7 (5.2 to 8.5) J/min (P = 0.091), respectively. ME was independently associated with postoperative pulmonary complications (ORadjusted, 1.44 [95% CI, 1.16 to 1.80]; P = 0.001). However, the association between time-weighted average MP and postoperative pulmonary complications was time-dependent, and time-weighted average MP was significantly associated with postoperative pulmonary complications in cases utilizing longer periods of mechanical ventilation (210 min or greater; ORadjusted, 1.46 [95% CI, 1.11 to 1.93]; P = 0.007). Normalization of ME and time-weighted average MP either to predicted body weight or to respiratory system compliance did not alter these associations.

CONCLUSIONS

ME and, in cases requiring longer periods of mechanical ventilation, MP were independently associated with postoperative pulmonary complications in thoracic surgery.

EDITOR’S PERSPECTIVE

Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.

Minimally invasive surgical approach in children treated for oesophageal atresia is associated with attention problems at school age: a prospective cohort study

The long-term neurodevelopment of children born with oesophageal atresia (OA) is unclear. Therefore, we assessed the neurocognitive domains and their predictors. Patients born with OA between February 2006 and December 2014, who were routinely seen at eight years as part of a structured prospective longitudinal follow-up program, were included. Main outcome measures were need for school support, performance in various neurocognitive domains and potential predictors of neurocognitive problems. We analysed data of 65 children with a mean (SD) age of 8.1 (0.2) years, of whom 89% with OA type C. Thirty-five (54%) surgical corrections were minimally invasive; the median (interquartile range) duration of exposure to anaesthetics in the first 24 months was 398 (296 - 710) minutes. Forty-four (68%) attended regular education without extra support and intelligence was within normal range (99-108). More than 50% had z-scores ≤ -2 on one or more neurocognitive domains, of which attention was the most frequently affected domain. The speed on the sustained attention task was significantly below normal (z-score -1.48 (2.12), p < .001), as was fluctuation of sustained attention (z-score -3.19 (3.80), p < .001). The minimally invasive approach and a lower socio-economic status (both p = 0.006) proved significant predictors for sustained attention problems in multivariable analyses.  Conclusion: Children who undergo minimally invasive surgery for OA correction are at risk for sustained attention problems at school age. Future studies unravelling the effects of perioperative events on neurodevelopment should lead to optimal surgical, anaesthesiological, and intensive care management in the neonatal period. What is Known: • School-aged children born with oesophageal atresia have normal intelligence but problems with sustained attention at eight years. What is New: • Oesophageal atresia patients, who undergo minimally invasive surgery or who have a background of lower socioeconomic status are at serious risk for sustained attention problems at school age. • Moreover, those who have been intubated for a longer period are at risk for stronger fluctuations in sustained attention.

Association of serum albumin with heart failure mortality with NYHA class IV in Chinese patients: Insights from PhysioNet database (version 1.3)

BACKGROUND

Studies have proved that low albumin level is associated with increased mortality in most diseases, such as chronic kidney disease and hepatic cirrhosis. However, the relationship between albumin and all-cause death in heart failure patients in China is still unclear.

OBJECTIVES

We aimed to investigate the association between albumin level and 28-day mortality in Chinese hospitalized patients with NYHA IV heart failure.

METHODS

A total of 2008 Chinese patients were included. The correlation between serum albumin level and mortality was tested using a cox proportional hazards regression model. The smooth curve fitting was used to identify non-linear relationships between serum albumin and mortality. The Forest plot analysis was used to assess the association between albumin and 28-day mortality in different groups.

RESULTS

Compared with patients with NYHA II-III, patients with NYHA IV had lower albumin level and higher mortality within 28 days. The albumin on admission was independently and inversely associated with the endpoint risk, which remained significant (hazard ratio: 0.80; 95 % confidence interval: 0.66 to 0.96; p = 0.0196) after multivariable adjustment. The smooth curve fitting showed with the increase of albumin, the mortality within 28 days would decrease. A subgroup analysis found that the inverse association between the albumin level and risk of the mortality was consistent across the subgroup stratified by possible influence factors.

CONCLUSION

Serum albumin level is negatively associated with 28-day mortality in hospitalized heart failure patients within NYHA IV in China, which can be used as an independent predictor.

Dexmedetomidine improves pulmonary outcomes in thoracic surgery under one-lung ventilation: A meta-analysis

INTRODUCTION

Dexmedetomidine improves intrapulmonary shunt in thoracic surgery and minimizes inflammatory response during one-lung ventilation (OLV). However, it is unclear whether such benefits translate into less postoperative pulmonary complications (PPCs). Our objective was to determine the impact of dexmedetomidine on the incidence of PPCs after thoracic surgery.

METHODS

Major databases were used to identify randomized trials that compared dexmedetomidine versus placebo during thoracic surgery in terms of PPCs. Our primary outcome was atelectasis within 7 days after surgery. Other specific PPCs included hypoxemia, pneumonia, and acute respiratory distress syndrome (ARDS). Secondary outcome included intraoperative respiratory mechanics (respiratory compliance [Cdyn]) and postoperative lung function (forced expiratory volume [FEV1]). Random effects models were used to estimate odds ratios (OR).

RESULTS

Twelve randomized trials, including 365 patients in the dexmedetomidine group and 359 in the placebo group, were analyzed in this meta-analysis. Patients in the dexmedetomidine group were less likely to develop postoperative atelectasis (2.3% vs 6.8%, OR 0.42, 95%CI 0.18-0.95, P = 0.04; low certainty) and hypoxemia (3.4% vs 11.7%, OR 0.26, 95%CI 0.10-0.68, P = 0.01; moderate certainty) compared to the placebo group. The incidence of postoperative pneumonia (3.2% vs 5.8%, OR 0.57, 95%CI 0.25-1.26, P = 0.17; moderate certainty) or ARDS (0.9% vs 3.5%, OR 0.39, 95%CI 0.07-2.08, P = 0.27; moderate certainty) was comparable between groups. Both intraoperative Cdyn and postoperative FEV1 were higher among patients that received dexmedetomidine with a mean difference of 4.42 mL/cmH2O (95%CI 3.13-5.72) and 0.27 L (95%CI 0.12-0.41), respectively.

CONCLUSION

Dexmedetomidine administration during thoracic surgery may potentially reduce the risk of postoperative atelectasis and hypoxemia. However, current evidence is insufficient to demonstrate an effect on pneumonia or ARDS.

Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model

BACKGROUND

Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging.

OBJECTIVE

We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths.

METHODS

A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed.

RESULTS

A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records.

CONCLUSION

This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data.

Effect of postoperative oxygen therapy regimen modification on oxygenation in patients with acute type A aortic dissection

Objective

In this study, we investigated the effect of various oxygen therapy regimens on oxygenation in patients with acute type A aortic dissection (AAD).

Methods

A quasi-randomized controlled trial was conducted, in which patients with AAD hospitalized for surgery from June to September 2021 were assigned to the control group (patients received conventional oxygen therapy after postoperative mechanical ventilation, weaning, and extubation) and those who were admitted from October to December 2021 were assigned to the observation group [patients underwent optimally adjusted therapy based on the treatment of the control group, which mainly included prioritized elevation of positive end-expiratory pressure (PEEP) and restricted use of the fraction of inspired oxygen (FiO2)].The postoperative oxygenation index, blood gas analysis, and duration of mechanical ventilation were compared between the two groups.

Results

There were significant differences in oxygenation observed at 2 h postoperatively between the groups. 12, 24, and 72 h postoperatively, the oxygenation index varied significantly between the two groups. There were statistically significant differences in the time effects of the oxygenation index and PaO2 between the two groups, as well as significant differences in the length of stay in the intensive care unit.

Conclusion

For the postoperative care of patients with AAD, it is suggested that the minimum FiO2 required for oxygenation of patients be maintained. In addition, it is possible to enhance PEEP as a priority when PaO2 is low.

Effect of Histamine H2 Receptor Antagonists on All-Cause Mortality in Critically Ill Patients With Essential Hypertension: A Retrospective Cohort Study

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

Impact of sarcopenia in elderly patients undergoing elective total hip arthroplasty on postoperative outcomes: a propensity score-matched study

OBJECTIVE

Frailty poses a crucial risk for postoperative complications in the elderly, with sarcopenia being a key component. The impact of sarcopenia on postoperative outcomes after total hip arthroplasty (THA) is still unclear. This study investigated the potential link between sarcopenia and postoperative outcomes among elderly THA patients.

METHODS

Totally 198 older patients were enrolled in this study. Sarcopenia in this group was determined by assessing the skeletal muscle index, which was measured using computed tomography at the 12th thoracic vertebra and analyzed semi-automatically with MATLAB R2020a. Propensity score matching (PSM) was employed to evaluate postoperative complications of grade II and above (POCIIs).

RESULTS

The variables balanced using PSM contained age, sex and comorbidities including hypertension, diabetes, hyperlipidemia and COPD. Before PSM, sarcopenic patients with reduced BMI (24.02 ± 0.24 vs. 27.11 ± 0.66, P < 0.001) showed higher POCIIs rates (48.31% vs. 15%, P = 0.009) and more walking-assisted discharge instances (85.96% vs. 60%, P = 0.017) compared with non-sarcopenia patients. After PSM, this group maintained reduced BMI (23.47 ± 0.85 vs. 27.11 ± 0.66, P = 0.002), with increased POCIIs rates (54.41% vs. 15%, P = 0.002) and heightened reliance on walking assistance at discharge (86.96% vs. 60%, P = 0.008).

CONCLUSION

Sarcopenia patients exhibited a higher incidence of POCIIs and poorer physical function at discharge. Sarcopenia could serve as a valuable prognostic indicator for elderly patients undergoing elective THA.

Flexible and Rigid Bronchoscopy for Critically Ill Children on Extracorporeal Membrane Oxygenation

BACKGROUND

We aim to describe our experience with bronchoscopy to diagnose and relieve tracheobronchial obstruction in anticipation of decannulation in children on extracorporeal membrane oxygenation (ECMO) support.

METHODS

A retrospective cohort study of children on ECMO between 1/2018 and 12/2022.

RESULTS

A total of 107 children required ECMO support during the study period for cardiac (n = 48, 45%), pulmonary (n = 38, 36%), or cardiopulmonary dysfunction (n = 21, 20%). Thirty-seven (35%) patients underwent 99 bronchoscopies while on ECMO. Most (76%, n = 75) experienced no improvement or worsening of chest radiography 24 hours following bronchoscopy. Clinical improvement in tidal volumes 48 hours after the first bronchoscopy was noted in 13/25 patients with available data (p = 0.05). Adverse events were seen in 18 (49%) patients who underwent bronchoscopy, including pneumothorax (n = 8, 22%), pneumonia (n = 7, 19%), pulmonary hemorrhage (n = 6, 16%), and sepsis (n = 5, 14%). ECMO courses were longer (25.4 ± 37.2 vs 6.1 ± 8.8 days, p < 0.0001) and more likely to be complicated by pneumonia (p = 0.0004) and sepsis (p = 0.047) in patients who underwent bronchoscopy compared with those who did not. Adverse events following bronchoscopy were associated with the number of bronchoscopies (p = 0.0003) and the presence of obstructive materials but not with the type of bronchoscopy or indication for ECMO. Mortality rates were similar between patients who underwent bronchoscopy and those who did not.

CONCLUSION

Children requiring bronchoscopy represent a subset of the sickest children on ECMO. Bronchoscopy may provide benefit in children with persistent cardiopulmonary failure who could not otherwise be decannulated. Adverse events are associated with the number of bronchoscopies and the presence of obstructive material.

LEVEL OF EVIDENCE

4 Laryngoscope, 2024.

Identifying novel acute pancreatitis sub-phenotypes using total serum calcium trajectories

BACKGROUND

Acute pancreatitis (AP) has heterogeneous clinical features, and identifying clinically relevant sub-phenotypes is useful. We aimed to identify novel sub-phenotypes in hospitalized AP patients using longitudinal total serum calcium (TSC) trajectories.

METHODS

AP patients had at least two TSC measurements during the first 24 h of hospitalization in the US-based critical care database (Medical Information Mart for Intensive Care-III (MIMIC-III) and MIMIC-IV were included. Group-based trajectory modeling was used to identify calcium trajectory phenotypes, and patient characteristics and treatment outcomes were compared between the phenotypes.

RESULTS

A total of 4518 admissions were included in the analysis. Four TSC trajectory groups were identified: "Very low TSC, slow resolvers" (n = 65; 1.4% of the cohort); "Moderately low TSC" (n = 559; 12.4%); "Stable normal-calcium" (n = 3875; 85.8%); and "Fluctuating high TSC" (n = 19; 0.4%). The "Very low TSC, slow resolvers" had the lowest initial, maximum, minimum, and mean TSC, and highest SOFA score, creatinine and glucose level. In contrast, the "Stable normal-calcium" had the fewest ICU admission, antibiotic use, intubation and renal replace treatment. In adjusted analysis, significantly higher in-hospital mortality was noted among "Very low TSC, slow resolvers" (odds ratio [OR], 7.2; 95% CI, 3.7 to 14.0), "moderately low TSC" (OR, 5.0; 95% CI, 3.8 to 6.7), and "Fluctuating high TSC" (OR, 5.6; 95% CI, 1.5 to 20.6) compared with the "Stable normal-calcium" group.

CONCLUSIONS

We identified four novel sub-phenotypes of patients with AP, with significant variability in clinical outcomes. Not only the absolute TSC levels but also their trajectories were significantly associated with in-hospital mortality.

Systemic inflammation and delirium during critical illness

PURPOSE

The purpose of this study was to determine associations between markers of inflammation and endogenous anticoagulant activity with delirium and coma during critical illness.

METHODS

In this prospective cohort study, we enrolled adults with respiratory failure and/or shock treated in medical or surgical intensive care units (ICUs) at 5 centers. Twice per day in the ICU, and daily thereafter, we assessed mental status using the Richmond Agitation Sedation Scale (RASS) and the Confusion Assessment Method-Intensive Care Unit (CAM-ICU). We collected blood samples on study days 1, 3, and 5, measuring levels of C-reactive protein (CRP), interferon gamma (IFN-γ), interleukin (IL)-1 beta (IL-1β), IL-6, IL-8, IL-10, IL-12, matrix metalloproteinase-9 (MMP-9), tumor necrosis factor-alpha (TNF-α), tumor necrosis factor receptor 1 (TNFR1), and protein C using validated protocols. We used multinomial logistic regression to analyze associations between biomarkers and the odds of delirium or coma versus normal mental status the following day, adjusting for age, sepsis, Sequential Organ Failure Assessment (SOFA), study day, corticosteroids, and sedatives.

RESULTS

Among 991 participants with a median age (interquartile range, IQR) of 62 [53-72] years and enrollment SOFA of 9 [7-11], higher concentrations of IL-6 (odds ratio [OR] [95% CI]: 1.8 [1.4-2.3]), IL-8 (1.3 [1.1-1.5]), IL-10 (1.5 [1.2-1.8]), TNF-α (1.2 [1.0-1.4]), and TNFR1 (1.3 [1.1-1.6]) and lower concentrations of protein C (0.7 [0.6-0.8])) were associated with delirium the following day. Higher concentrations of CRP (1.4 [1.1-1.7]), IFN-γ (1.3 [1.1-1.5]), IL-6 (2.3 [1.8-3.0]), IL-8 (1.8 [1.4-2.3]), and IL-10 (1.5 [1.2-2.0]) and lower concentrations of protein C (0.6 [0.5-0.8]) were associated with coma the following day. IL-1β, IL-12, and MMP-9 were not associated with mental status.

CONCLUSION

Markers of inflammation and possibly endogenous anticoagulant activity are associated with delirium and coma during critical illness.

Nomogram for predicting in-hospital mortality in trauma patients undergoing resuscitative endovascular balloon occlusion of the aorta: a retrospective multicenter study

Recently, resuscitative endovascular balloon occlusion of the aorta (REBOA) had been introduced as an innovative procedure for severe hemorrhage in the abdomen or pelvis. We aimed to investigate risk factors associated with mortality after REBOA and construct a model for predicting mortality. This multicenter retrospective study collected data from 251 patients admitted at five regional trauma centers across South Korea from 2015 to 2022. The indications for REBOA included patients experiencing hypovolemic shock due to hemorrhage in the abdomen, pelvis, or lower extremities, and those who were non-responders (systolic blood pressure (SBP) < 90 mmHg) to initial fluid treatment. The primary and secondary outcomes were mortality due to exsanguination and overall mortality, respectively. After feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting, a multivariate logistic regression (MLR) model and nomogram were constructed. In the MLR model using risk factors selected in the LASSO, five risk factors, including initial heart rate (adjusted odds ratio [aOR], 0.99; 95% confidence interval [CI], 0.98-1.00; p = 0.030), initial Glasgow coma scale (aOR, 0.86; 95% CI 0.80-0.93; p < 0.001), RBC transfusion within 4 h (unit, aOR, 1.12; 95% CI 1.07-1.17; p < 0.001), balloon occlusion type (reference: partial occlusion; total occlusion, aOR, 2.53; 95% CI 1.27-5.02; p = 0.008; partial + total occlusion, aOR, 2.04; 95% CI 0.71-5.86; p = 0.187), and post-REBOA systolic blood pressure (SBP) (aOR, 0.98; 95% CI 0.97-0.99; p < 0.001) were significantly associated with mortality due to exsanguination. The prediction model showed an area under curve, sensitivity, and specificity of 0.855, 73.2%, and 83.6%, respectively. Decision curve analysis showed that the predictive model had increased net benefits across a wide range of threshold probabilities. This study developed a novel intuitive nomogram for predicting mortality in patients undergoing REBOA. Our proposed model exhibited excellent performance and revealed that total occlusion was associated with poor outcomes, with post-REBOA SBP potentially being an effective surrogate measure.

Preconceptional maternal hyperandrogenism and metabolic syndrome risk in male offspring: a long-term population-based study

PURPOSE

There is limited research on the effects of maternal hyperandrogenism (MHA) on cardiometabolic risk factors in male offspring. We aimed to compare the risk of metabolic syndrome (MetS) in sons of women with preconceptional hyperandrogenism (HA) to those of non-HA women in later life.

METHODS

Using data obtained from the Tehran Lipid and Glucose Cohort Study, with an average of 20 years follow-up, 1913 sons were divided into two groups based on their MHA status, sons with MHA (n = 523) and sons without MHA (controls n = 1390). The study groups were monitored from the baseline until either the incidence of events, censoring, or the end of the study period, depending on which occurred first. Age-scaled unadjusted and adjusted Cox regression models were utilized to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between MHA and MetS in their sons.

RESULTS

There was no significant association between MHA and HR of MetS in sons with MHA compared to controls, even after adjustment (unadjusted HR (95% CI) 0.94 (0.80-1.11), P = 0.5) and (adjusted HR (95% CI) 0.98 (0.81-1.18), P = 0.8). Sons with MHA showed a HR of 1.35 for developing high fasting blood sugar compared to controls (unadjusted HR (95% CI) 1.35 (1.01-1.81), P = 0.04), however, after adjustment this association did not remain significant (adjusted HR (95% CI) 1.25 (0.90-1.74), P = 0.1).

CONCLUSION

The results suggest that preconceptional MHA doesn't increase the risk of developing MetS in sons in later life. According to this suggestion, preconceptional MHA may not have long-term metabolic consequences in male offspring.

Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study

This study explores the bidirectional association between multimorbidity and falls in Chinese middle-aged and elderly adults. Participants aged 45 and above from the China Health and Retirement Longitudinal Study were included. Binary logistic regression assessed the impact of chronic conditions on fall incidence (stage I), while multinomial logistic regression examined the relationship between baseline falls and multimorbidity (stage II). The fully adjusted odds ratios (ORs) for one, two, or three or more chronic conditions were 1.34, 1.65, and 2.02, respectively. Among participants without baseline falls, 28.61% developed two or more chronic conditions during follow-up, compared to 37.4% of those with a history of falls. Fully adjusted ORs for one, two, or three or more chronic conditions in those with a history of falls were 1.21, 1.38 and 1.70, respectively. The bidirectional relationship held in sensitivity and subgroup analyses. A bidirectional relationship exists between multimorbidity and falls in Chinese middle-aged and elderly adults. Strengthening chronic condition screening and treatment in primary healthcare may reduce falls risk, and prioritizing fall prevention and intervention in daily life is recommended.

Meta-analysis of evaluating neuron specific enolase as a serum biomarker for sepsis-associated encephalopathy

INTRODUCTION

Brain dysfunction in sepsis is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Neuron specific enolase (NSE) may serve as an important neurocritical biomarker for detection and longitudinal monitoring in SAE patients. Our Meta-analysis aimed to explore the diagnostic and prognostic value of serum NSE in SAE patients. Currently, no systematic Review and Meta-analysis have been assessed that NSE as a biomarker of SAE.

METHODS

The study protocol was registered in the PROSPERO database (CRD42023398736) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a systematic review and Meta-analysis to evaluate the serum NSE's diagnostic accuracy for SAE and prognostic strength for probability of death of septic patients. We systematic searched electronic bibliographic databases from PubMed, MEDLINE, Web of Science, Embase, Cochrane databases, CNKI, CQVIP, and WFSD. QUADAS-2 assessment tool was used to evaluate quality and risk of bias of the selected studies. Subgroup analyses, funnel plots, sensitivity analyses were also carried out. Review Manager version 5.4 and Stata16.0. was used for statistical analysis.

RESULTS

This Meta-analysis included 22 studies with 1361 serum samples from SAE patients and 1580 serum samples from no-encephalopathy septic (NE) patients. The Meta-analysis showed that individuals with SAE had higher serum NSE level than NE controls (SMD 1.93 (95 % CI 1.51-2.35), P < 0.00001). In addition, there are 948 serum samples from survival septic patients and 446 serum samples from non-survival septic patients, septic patients with survival outcomes had lower serum NSE levels than those with death outcomes (SMD -1.87 (95 % CI -2.43 to -1.32), P < 0.00001).

CONCLUSION

Our Meta-analysis reveals a significant association between elevated NSE concentrations and the increased likelihood of concomitant SAE and mortality during septic patients. This comprehensive analysis will equip ICU physicians with up-to-date insights to accurately identify patients at risk of SAE and implement appropriate intervention strategies to mitigate morbidity and improve neurological outcomes. However, it is important to note that the presence of substantial heterogeneity among studies poses challenges in determining the most effective discrimination cutoff values and optimal sampling collection time.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning

BACKGROUND

Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization.

METHODS AND RESULTS

The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure.

CONCLUSIONS

Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

BACKGROUND

Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies.

OBJECTIVE

This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs).

METHODS

We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families.

RESULTS

A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope.

CONCLUSIONS

This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Analysis of fecal microbiota and related clinical indicators in ICU patients with sepsis

Background

To analyze the characteristics of fecal microbiota disturbance in the intensive care unit (ICU) patients with sepsis and the correlation with related clinical indicators.

Methods

This study included 31 patients with sepsis admitted to the emergency ICU ward between September 2019 and December 2021. They were divided into Group without septic shock (ND_NS group, 7 cases) and Group with septic shock (ND_S group, 24 cases) according to the presence or absence of septic shock. Furthermore, we divided these 31 sepsis patients into Clinical Improvement group (21 cases) and Death or DAMA group (10 cases) based on clinical outcome, 15 cases of Physical Examiner recruited in the same period were included as control group: ND_HC group (15 cases). The fecal samples of the patients with sepsis within 24 h of admission and random fecal samples of the control group were collected and analyzed by 16S rDNA gene sequencing used for the analysis of fecal microbiota. At the same time, the relevant clinical data of these patients with sepsis were also collected for analysis.

Results

There were 15 cases with drug-resistant bacteria in the ND_S group and only 2 cases in the ND_NS group (P = 0.015). There were significant differences in APACHE II score, length of ICU stay, lactate level, and oxygenation index of patients between the Death or DAMA group and Clinical Improvement group (all P < 0.05). For phylum level, the abundance of Firmicutes, Actinobacteria, and Bacteroidetes decreased in the ND group compared with the ND_HC group, while the abundance of Proteobacteria increased (P < 0.05). For genus level, the relative abundance of Escherichia-Shigella and Klebsiella were significantly increased in the ND group compared with the ND_HC group (P < 0.05). The top six genera in relative abundance in the ND_S group were Escherichia-Shigella, Enterococcus, Bifidobacterium, Lactobacillus, Akkermansia, and Klebsiella. Compared with the Clinical Improvement group, the relative abundance of Escherichia-Shigella and Klebsiella in the Death or DAMA group showed an increasing trend with no significant significance, while the relative abundance of Enterococcus and Faecalibacterium decreased in the Death or DAMA group (P < 0.05). Alpha diversity analysis showed that compared with the ND_HC group, the alpha diversity of the fecal microbiota in the ND group decreased. There were significant differences in the Observed_species index, Chao1 index, and ACE index of patients between the ND_HC group and ND group (all P < 0.05). Moreover, compared with the ND_NS group, the Alpha diversity of the ND_S group was more abundant. PCoA analysis showed significant differences in microbial community structure between the ND group and ND_HC group (P = 0.001). There also were significant differences in microbial community structure between the ND_S group and ND_NS group (P = 0.008). LEfSe analysis showed that compared with the ND_HC group, there were significant differences in the species of the ND group, including Enterobacteriaceae, Escherichia-Shigella, Enterococcus, Elizabethkingia, and Family_XIII_AD3011_group.

Conclusions

ICU patients with sepsis suffered intestinal microecological disturbances with significantly decreased abundance of fecal microbiota, diversity, and beneficial symbiotic bacteria. For these patients, the ratio of pathogenic bacteria, including Escherichia-Shigella and Klebsiella increased and became the main bacterial genus in some samples. Moreover, the increasing trend of these two pathogenic bacteria may be correlated with the development of septic shock and the risk of death in patients with sepsis.

Impact of dementia status on intravenous thrombolysis and endovascular treatment for acute ischemic stroke: Retrospective study

INTRODUCTION

Individuals with dementia are underrepresented in interventional studies for acute ischemic stroke (AIS). This research gap creates a bias against their treatment in clinical practice. Our goal was to compare the safety and efficacy of intravenous-thrombolysis (t-PA) and endovascular treatment (EVT) in individuals with or without pre-AIS dementia.

METHOD

A retrospective study of AIS patients receiving t-PA or EVT between 2019 and 2022. Patients were classified as dementia on a case-by-case review of baseline assessment. Additional variables included demographic, vascular risk factors, AIS severity and treatment. Outcomes of interest were intracerebral hemorrhage, mortality in 90-days, and the difference in modified rankin scale (mRS) before AIS and in 90-days follow-up. Outcomes were compared across non-matched groups and following propensity-score matching.

RESULTS

Altogether, 628 patients were included, of which 68 had pre-AIS dementia. Compared to non-dementia group, dementia group were older, had a higher rate of vascular risk factors, higher pre-stroke mRS and higher baseline NIHSS. Individuals with dementia had higher rates of mortality (25% vs.11%,p < 0.01) on non-matched comparison. All cohort and restricted t-PA EVT matched analysis showed no difference in any outcome. Regression analysis confirmed that AIS severity at presentation and its treatment, not dementia, were the chief contributors to patients' outcomes.

DISCUSSION

Our results indicate that pre-AIS dementia does not impact the efficacy or safety of EVT or t-PA for AIS. We thus call for more inclusive research on stroke therapy with regards to baseline cognitive status. Such studies are urgently required to inform stroke guidelines and enhance care.

Deep-learning-based real-time individualization for reduce-order haemodynamic model

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application.

Comparison of left- and right-sided colorectal cancer to explore prognostic signatures related to pyroptosis

Background

Colorectal cancer (CRC) is one of the most common malignancies, and pyroptosis exerts an immunoregulatory role in CRC. Although the location of the primary tumor is a prognostic factor for patients with CRC, the mechanisms of pyroptosis in left- and right-sided CRC remain unclear.

Methods

Expression and clinical data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differences in clinical characteristics, immune cell infiltration, and somatic mutations between left- and right-sided CRC were then compared. After screening for differentially expressed genes, Pearson correlation analysis was performed to select pyroptosis-related genes, followed by a gene set enrichment analysis. Univariate and multivariate Cox regression analyses were used to construct and validate the prognostic model and nomogram for predicting prognosis. Collected left- and right-sided CRC samples were subjected to reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key pyroptosis-related genes.

Results

Left- and right-sided CRC exhibited significant differences in clinical features and immune cell infiltration. Five prognostic signatures were identified from among 134 pyroptosis-related differentially expressed genes to construct a risk score-based prognostic model, and adverse outcomes for high-risk patients were further verified using an external cohort. A nomogram was also generated based on three independent prognostic factors to predict survival probabilities, while calibration curves confirmed the consistency between the predicted and actual survival. Experiment data confirmed the significant differential expression of five genes between left- and right-sided CRC.

Conclusion

The five identified pyroptosis-related gene signatures may be potential biomarkers for predicting prognosis in left- and right-sided CRC and may help improve the clinical outcomes of patients with CRC.

Development and validation of a predicting nomogram for in-hospital mortality of COVID-19 Omicron variant: A cohort study of 1324 cases in Beijing Anzhen Hospital

Coronavirus disease 2019 (COVID-19) is continuously posing high global public health concerns due to its high morbidity and mortality. This study aimed to construct a convenient risk model for predicting in-hospital mortality of COVID-19 Omicron variant. A total of 1324 hospitalized patients with Omicron variant were enrolled from Beijing Anzhen Hospital. During hospitalization, the Omicron variant mortality rate was found to be 24.4%. Using the datasets of clinical demographics and laboratory tests, three machine learning algorithms, including best subset selection, stepwise selection, and least absolute shrinkage and selection operator regression analyses were employed to identify the potential predictors of in-hospital mortality. The results found that a panel of twenty-four clinical variables (including age, hyperlipemia, stroke, tumor, and several cardiovascular markers) identified by stepwise selection model exhibited significant performances in predicting the in-hospital mortality of COVID-19. The resultant nomogram showed good discrimination, highlighted by the areas under the curve values of 0.88 for 10 days, 0.81 for 20 days, and 0.82 for 30 days, respectively. Furthermore, decision curve analysis showed a significant reliability and precision for the established stepwise selection model. Collectively, this study developed an accurate and convenience risk model for predicting the in-hospital mortality of COVID-19 Omicron.

Sodium bicarbonate and intubation in severe diabetic ketoacidosis: are we too quick to dismiss them?

Management of diabetic ketoacidosis (DKA) has internationally established guidelines. However, management of severe, refractory DKA with multiple contributors to acidosis, and management of DKA in patients with altered mentation, remain ambiguous. Use of sodium bicarbonate and intubation in DKA are unpopular treatment practices, but warrant consideration in these unique clinical scenarios. This paper describes a 61-year-old Sri Lankan female who presented with severe DKA, seizures and altered level of consciousness. In her case, the acidosis was secondary to DKA, hyperlactatemia, hyperchloraemic acidosis and acute kidney injury (AKI). Intravenous sodium bicarbonate was used in the management of acidosis. She was intubated due to altered level of consciousness with inadequate respiratory drive to compensate for metabolic acidosis. The outcome in her case was favorable. Intravenous sodium bicarbonate in DKA should be considered for patients with severe, refractory acidosis with hemodynamic instability, hyperkalemia and compounding acidosis due to normal anion gap acidosis or AKI. Intubation should be considered for patients with obtunded mentation unable to achieve respiratory compensation and obtunded mentation where reversal of DKA is unlikely to improve consciousness. Both strategies should be personalized with consideration of individual risk vs benefit.

Machine learning-based prognostication of mortality in stroke patients

Objectives

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.

Materials and methods

Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.

Results

The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.

Conclusions

This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Appraisal of National Institute for Health and Care Research activity in primary care in England: cross-sectional study

BACKGROUND

The National Institute for Health and Care Research (NIHR) was set up to enhance clinical and health research activity in a variety of National Health Service (NHS) healthcare settings, including primary care.

OBJECTIVE

To appraise how overall General Practitioner (GP) practice performance, location, and staffing levels may interact with NIHR Portfolio activity in primary care in England.

METHODS

Cross-sectional summary of GP practice research activity and practice descriptors; complete data from 6,171 GP practices was collated from NIHR (using data for 2013-2023 for Portfolio studies), Public Health England, Care Quality Commission, and NHS Digital sources, respectively.

RESULTS

In primary care, 1 million patients have been recruited into NIHR Portfolio studies in the last decade. The top 10% of practices-measured by different studies recruited to-contributed over 50% of that accrual. When the top decile of GP practices is compared to the 20% least active GP practices, research activity is significantly and individually linked with larger GP practices. Furthermore, it is significantly yet modestly associated with GP practice performance (positive patient feedback, Care Quality Commission rating), lower locality deprivation levels, and lower patient to GP ratios.

CONCLUSIONS

Research activity in GP practices is-as seen previously with hospitals-significantly linked with better GP practice performance and patient feedback. Practice list size and staffing levels in particular interact with the aforementioned. This should be taken into account when determining strategies to increase patient and GP practice participation in NIHR Portfolio research studies.

A strategy for oral delivery of FGF21 for mitigating inflammation and multi-organ damage in sepsis

Fibroblast growth factor 21 (FGF21) shows great therapeutic potential in metabolic, neurodegenerative and inflammatory diseases. However, current FGF21 administration predominantly relies on injection rather than oral ingestion due to its limited stability and activity post-gastrointestinal transit, thereby hindering its clinical utility. Milk-derived exosomes (mEx) have emerged as a promising vehicle for oral drug delivery due to their ability to maintain structural integrity in the gastrointestinal milieu. To address the challenge associated with oral delivery of FGF21, we encapsulated FGF21 within mEx (mEx@FGF21) to protect its activity post-oral administration. Additionally, we modified the surface of mEx@FGF21 by introducing transferrin (TF) to enhance intestinal absorption and transport, designated TF-mEx@FGF21. In vitro results demonstrated that the surface modification of TF promoted FGF21 internalization by intestinal epithelial cells. Orally administered TF-mEx@FGF21 showed promising therapeutic effects in septic mice. This study represents a practicable strategy for advancing the clinical application of oral FGF21 delivery.

Mean airway pressure as a parameter of lung-protective and heart-protective ventilation

Mean airway pressure (MAP) is the mean pressure generated in the airway during a single breath (inspiration + expiration), and is displayed on most anaesthesia and intensive care ventilators. This parameter, however, is not usually monitored during mechanical ventilation because it is poorly understood and usually only used in research. One of the main determinants of MAP is PEEP. This is because in respiratory cycles with an I:E ratio of 1:2, expiration is twice as long as inspiration. Although MAP can be used as a surrogate for mean alveolar pressure, these parameters differ considerably in some situations. Recently, MAP has been shown to be a useful prognostic factor for respiratory morbidity and mortality in mechanically ventilated patients of various ages. Low MAP has been associated with a lower incidence of 90-day mortality, shorter ICU stay, and shorter mechanical ventilation time. MAP also affects haemodynamics: there is evidence of a causal relationship between high MAP and low perfusion index, both of which are associated with poor prognosis in mechanically ventilated patients. Elevated MAP values have also been associated with high central venous pressure and lactate, which are indicative of ventilator-associated right ventricular failure and tissue hypoperfusion, respectively. MAP, therefore, is an important parameter to measure in clinical practice. The aim of this review has been to identify the determinants of MAP, the pros and cons of using MAP instead of traditional protective ventilation parameters, and the evidence that supports the use of MAP in clinical practice.

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Parenteral Nutrition in the Critically Ill Adult: A Narrative Review

Malnutrition in adult intensive care unit patients is associated with poor clinical outcomes. Providing adequate nutritional support to the critically ill adult should be an important goal for the intensivist. This narrative review aims to delineate the role of parenteral nutrition (PN) in meeting nutritional goals. We examined the data regarding the safety and efficacy of PN compared to enteral nutrition. In addition, we describe practical considerations for the use of PN in the ICU including patient nutritional risk stratification, nutrient composition selection for PN, route of PN administration, and biochemical monitoring.

Using Mendelian randomization provides genetic insights into potential targets for sepsis treatment

Sepsis is recognized as a major contributor to the global disease burden, but there is a lack of specific and effective therapeutic agents. Utilizing Mendelian randomization (MR) methods alongside evidence of causal genetics presents a chance to discover novel targets for therapeutic intervention. MR approach was employed to investigate potential drug targets for sepsis. Pooled statistics from IEU-B-4980 comprising 11,643 cases and 474,841 controls were initially utilized, and the findings were subsequently replicated in the IEU-B-69 (10,154 cases and 454,764 controls). Causal associations were then validated through colocalization. Furthermore, a range of sensitivity analyses, including MR-Egger intercept tests and Cochran's Q tests, were conducted to evaluate the outcomes of the MR analyses. Three drug targets (PSMA4, IFNAR2, and LY9) exhibited noteworthy MR outcomes in two separate datasets. Notably, PSMA4 demonstrated not only an elevated susceptibility to sepsis (OR 1.32, 95% CI 1.20-1.45, p = 1.66E-08) but also exhibited a robust colocalization with sepsis (PPH4 = 0.74). According to the present MR analysis, PSMA4 emerges as a highly encouraging pharmaceutical target for addressing sepsis. Suppression of PSMA4 could potentially decrease the likelihood of sepsis.

Effects of restrictive fluid therapy on the time to resolution of hyperlactatemia in ICU patients with septic shock. A secondary post hoc analysis of the CLASSIC randomized trial

PURPOSE

The aim of this study was to examine the effects of intravenous (IV) fluid restriction on time to resolution of hyperlactatemia in septic shock. Hyperlactatemia in sepsis is associated with worse outcome. Sepsis guidelines suggest targeting lactate clearance to guide fluid therapy despite the complexity of hyperlactatemia and the potential harm of fluid overload.

METHODS

We conducted a post hoc analysis of serial plasma lactate concentrations in a sub-cohort of 777 patients from the international multicenter clinical CLASSIC trial (restriction of intravenous fluids in intensive care unit (ICU) patients with septic shock). Adult ICU patients with septic shock had been randomized to restrictive (n = 385) or standard (n = 392) intravenous fluid therapy. The primary outcome, time to resolution of hyperlactatemia, was analyzed with a competing-risks regression model. Death and discharge were competing outcomes, and administrative censoring was imposed 72 h after randomization if hyperlactatemia persisted. The regression analysis was adjusted for the same stratification variables and covariates as in the original CLASSIC trial analysis.

RESULTS

The hazard ratios (HRs) for the cumulative probability of resolution of hyperlactatemia, in the restrictive vs the standard group, in the unadjusted analysis, with time split, were 0.94 (confidence interval (CI) 0.78-1.14) at day 1 and 1.21 (0.89-1.65) at day 2-3. The adjusted analyses were consistent with the unadjusted results.

CONCLUSION

In this post hoc retrospective analysis of a multicenter randomized controlled trial (RCT), a restrictive intravenous fluid strategy did not seem to affect the time to resolution of hyperlactatemia in adult ICU patients with septic shock.

Abdominal physical examinations in early stages benefit critically ill patients without primary gastrointestinal diseases: a retrospective cohort study

Background

Gastrointestinal (GI) function is critical for patients in intensive care units (ICUs). Whether and how much critically ill patients without GI primary diseases benefit from abdominal physical examinations remains unknown. No evidence from big data supports its possible additive value in outcome prediction.

Methods

We performed a big data analysis to confirm the value of abdominal physical examinations in ICU patients without GI primary diseases. Patients were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and classified into two groups depending on whether they received abdominal palpation and auscultation. The primary outcome was the 28-day mortality. Statistical approaches included Cox regression, propensity score matching, and inverse probability of treatment weighting. Then, the abdominal physical examination group was randomly divided into the training and testing cohorts in an 8:2 ratio. And patients with GI primary diseases were selected as the validation group. Several machine learning algorithms, including Random Forest, Gradient Boosting Decision Tree, Adaboost, Extra Trees, Bagging, and Multi-Layer Perceptron, were used to develop in-hospital mortality predictive models.

Results

Abdominal physical examinations were performed in 868 (2.63%) of 33,007 patients without primary GI diseases. A significant benefit in terms of 28-day mortality was observed among the abdominal physical examination group (HR 0.75, 95% CI 0.56-0.99; p = 0.043), and a higher examination frequency was associated with improved outcomes (HR 0.62, 95%CI 0.40-0.98; p = 0.042). Machine learning studies further revealed that abdominal physical examinations were valuable in predicting in-hospital mortality. Considering both model performance and storage space, the Multi-Layer Perceptron model performed the best in predicting mortality (AUC = 0.9548 in the testing set and AUC = 0.9833 in the validation set).

Conclusion

Conducting abdominal physical examinations improves outcomes in critically ill patients without GI primary diseases. The results can be used to predict in-hospital mortality using machine learning algorithms.

Suicide While Locked Up in Texas: Risk Factors for Death by Suicide in Custody

In the United States, suicide is a leading cause of death in prisons and jails, with incarcerated individuals being nine times more likely to die by suicide than the general population. Identifying vulnerabilities at each stage of custody (prebooking, jail, prison) and factors that increase suicide risk can improve prevention efforts. A hierarchical binary logistic regression was conducted on data from the Texas Justice Initiative's Deaths in Custody Report. Variables included race/ethnicity, sex, age at death, days in custody, classification of crime as violent or nonviolent, and custody type of prebooking, jail, or prison. Among main effects, when compared to suicide rates in prison, jail suicide deaths were over three and a half times more likely (OR = 3.61), and the period of prebooking emerged as a period of staggering risk of suicide death, with suicides being over 5,000% more likely than at other stages of custody (OR = 50.86). When interactions were entered, Latinx individuals were at a particularly increased risk of suicide death (OR = 10.46), likelihood of suicide death decreased with each year of age (OR = .89), nonviolent offenders were just under three and a half times more likely to die by suicide when compared to violent offenders (OR = 3.45), and each stage of custody was shown to affect the relationship between age-related rates of suicide in different ways. Results call for further investigation into suicide among understudied populations in corrections, such as Latinx individuals, juveniles in the prison system, and nonviolent offenders, to identify the groups at the highest risk of premature death in correctional systems.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection

OBJECTIVES

To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery.

METHODS

We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method.

RESULTS

In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2.

CONCLUSIONS

CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Associations between life's essential 8 and metabolic health among us adults: insights of NHANES from 2005 to 2018

BACKGROUND

Metabolic unhealth (MUH) is closely associated with cardiovascular disease (CVD). Life's Essential 8 (LE8), a recently updated cardiovascular health (CVH) assessment, has some overlapping indicators with MUH but is more comprehensive and complicated than MUH. Given the close relationship between them, it is important to compare these two measurements.

METHODS

This population-based cross-sectional survey included 20- to 80-year-old individuals from 7 National Health and Nutrition Examination Survey (NHANES) cycles between 2005 and 2018. Based on the parameters provided by the American Heart Association, the LE8 score (which ranges from 0 to 100) was used to classify CVH into three categories: low (0-49), moderate (50-79), and high (80-100). The MUH status was evaluated by blood glucose, blood pressure, and blood lipids. The associations were assessed by multivariable regression analysis, subgroup analysis, restricted cubic spline models, and sensitivity analysis.

RESULTS

A total of 22,582 participants were enrolled (median of age was 45 years old), among them, 11,127 were female (weighted percentage, 49%) and 16,595 were classified as MUH (weighted percentage, 73.5%). The weighted median LE8 scores of metabolic health (MH) and MUH individuals are 73.75 and 59.38, respectively. Higher LE8 scores were linked to lower risks of MUH (odds ratio [OR] for every 10 scores increase, 0.53; 95% CI 0.51-0.55), and a nonlinear dose-response relationship was seen after the adjustment of potential confounders. This negative correlation between LE8 scores, and MUH was strengthened among elderly population.

CONCLUSIONS

Higher LE8 and its subscales scores were inversely and nonlinearly linked with the lower presence of MUH. MUH is consistent with LE8 scores, which can be considered as an alternative indicator when it is difficult to collect the information of health behaviors.

Improving predictions: Enhancing in-hospital mortality forecast for ICU patients with sepsis-induced coagulopathy using a stacking ensemble model

The incidence of sepsis-induced coagulopathy (SIC) is high, leading to increased mortality rates and prolonged hospitalization and intensive care unit (ICU) stays. Early identification of SIC patients at risk of in-hospital mortality can improve patient prognosis. The objective of this study is to develop and validate machine learning (ML) models to dynamically predict in-hospital mortality risk in SIC patients. A ML model is established based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict in-hospital mortality in SIC patients. Utilizing univariate feature selection for feature screening. The optimal model was determined by calculating the area under the curve (AUC) with a 95% confidence interval (CI). The optimal model was interpreted using Shapley Additive Explanation (SHAP) values. Among the 3112 SIC patients included in MIMIC-IV, a total of 757 (25%) patients experienced mortality during their ICU stay. Univariate feature selection helps us to pick out the 20 most critical variables from the original feature. Among the 10 developed machine learning models, the stacking ensemble model exhibited the highest AUC (0.795, 95% CI: 0.763-0.827). Anion gap and age emerged as the most significant features for predicting the mortality risk in SIC. In this study, an ML model was constructed that exhibited excellent performance in predicting in-hospital mortality risk in SIC patients. Specifically, the stacking ensemble model demonstrated superior predictive ability.

Low long-lasting insecticidal net use in malaria elimination areas in Southern Ethiopia: results from community based cross-sectional study

BACKGROUND

Despite remarkable progress in malaria burden reduction, malaria continues to be a major public health problem globally. Ethiopia has been distributing long-lasting insecticidal nets (LLINs) for free and nationwide distribution was completed in 2016. However, evidence suggests that the utilization of LLINs varies from setting to setting and from time to time due to different factors, and up-to-date evidence is required for LLIN related decision-making. Hence, this study was designed to assess LLIN utilization and its determinants in the Southern Nations, Nationalities, and People's Region (SNNPR) of Ethiopia.

METHODS

A community-based cross-sectional study was conducted in Southern Ethiopia in 2019. Using multi-stage sampling, a total of 2466 households were included. The region was stratified based on the annual malaria index as high, moderate, low, and free strata. Cluster sampling was then applied to select households from high, moderate, and low strata. Data on LLIN ownership, utilization and different determinant factors were collected using household questionnaire. SurveyCTO was used to collect data and data was managed using Stata 15. Descriptive statistics and multilevel mixed-effects logistic regression were performed to identify the determinants of utilization of LLINs. Effect measures were reported using adjusted odds ratio (AOR) with 95% CI.

RESULTS

From a total of 2466 households, 48.7% of households had at least one LLIN. LLIN adequacy based on family size was 23% while it was15.7% based on universal access and 29.2% based on sleeping space. From 1202 households that possessed LLIN(s), 66.0% of households reported that they slept under LLIN the night preceding the survey. However, when the total population in all surveyed households were considered, only 22.9% of household members slept under LLIN the night preceding the survey. Malaria endemicity, educational status, wealth status, and knowledge about malaria were associated with LLINs utilization. In addition, reasons for non-use included perceived absence of malaria, side effects of LLIN, conditions of LLINs, inconvenient space and low awareness.

CONCLUSION

Low LLIN coverage and low utilization were noted. A low level of utilization was associated with malaria endemicity, wealth status and level of awareness. Distribution of LLIN and continuous follow-up with community awareness creation activities are vital to improve coverage and utilization of LLINs, and to ensure the country's malaria elimination goal.

Perceived Barriers to Physical Activity Among Youth Living in Rural and Urban Canadian Communities: A Nationally Representative Cross-Sectional Study

BACKGROUND

We used nationally representative data to explore associations among location of residence (rural/urban) and perceived barriers to physical activity (PA) in Canadian youth.

METHODS

We analyzed the 2017 Canadian Community Health Survey, Barriers to Physical Activity Rapid Response data for 12- to 17-year-old youth. Nine items from the survey assessing perceived barriers to PA were combined into 3 barrier domains: resources, motivational, and socioenvironmental. The likelihood of reporting barriers to PA based on rural-urban location was examined using survey-weighted binary logistic regression following a model fitting approach. Sociodemographic factors were modeled as covariates and tested in interaction with location. For each barrier domain, we derived the best-fitting model with fewest terms.

RESULTS

There were no location-specific effects related to reporting any barrier or motivation-related PA barriers. We found a sex by location interaction predicting the likelihood of reporting resource-related barriers. Rural boys were less likely to report resource-related barriers compared with urban boys (odds ratio [OR] = 0.42 [0.20, 0.88]). Rural girls were more likely to report resource-related barriers compared with boys (OR = 3.72 [1.66, 8.30]). Regarding socioenvironmental barriers, we observed a significant body mass index by location interaction demonstrating that rural youth with body mass index outside the "normal range" showed a higher likelihood of reporting socioenvironmental barriers compared with urban youth (OR = 2.38 [1.32, 4.30]). For urban youth, body mass index was unrelated to reporting socioenvironmental barriers (OR = 1.07 [0.67, 1.71]).

CONCLUSION

PA barriers are not universal among Canadian youth. Our analyses highlight the importance of testing interactions in similar studies as well as considering key sociodemographic characteristics when designing interventions.

Classifying early infant feeding status from clinical notes using natural language processing and machine learning

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.