DNA-based FeCuAg nanoclusters with peroxidase-like and GSH depletion activities for toxicity of in vitro cancer cells.

Developing the efficient nanozymes for reactive oxygen species (ROS)-mediated highly potent tumor catalytic therapy has become a great challenge. In this study, we prepared the DNA-Fe, -FeAg, and -FeCuAg nanocluster (NCs) using the G-/C-rich single-stranded DNA (ssDNA) templates. The steady-state kinetic and the catalytic performances and mechanisms of DNA-metal NCs were first systematically investigated. The results indicated that c-kit-TBA-Fe, c-kit-TBA-FeAg, and c-kit-TBA-FeCuAg NCs exhibited the high peroxidase-like activity. All of three types of NCs presented the higher affinity to the substrate TMB and better storage stability at 4 °C than horseradish peroxidase (HRP). Moreover, c-kit-TBA-FeAg and c-kit-TBA-FeCuAg NCs presented the 6.7- and 4.7-fold stronger affinity to TMB than c-kit-TBA-Fe, respectively. However, the maximum reaction rate (Vmax) of c-kit-TBA-FeCuAg NCs with H2O2 was the largest, which promoted the generation of much more •OH in the reaction system. More importantly, c-kit-TBA-FeCuAg NCs were able to deplete largely the intracellular GSH and thus generate lots of endogenous ROS in HeLa cells, thereby exhibiting the significant and specific in vitro cancer cells toxicity. Therefore, c-kit-TBA-FeCuAg NCs, with peroxidase-like activity and glutathione (GSH) consumption ability, hold the ROS-based promising therapeutic effects for cancer.

Mining Heat-Resistant Key Genes of Peony Based on Weighted Gene Co-Expression Network Analysis.

The RNA-Seq and gene expression data of mature leaves under high temperature stress of Paeonia suffruticosa 'Hu Hong' were used to explore the key genes of heat tolerance of peony. The weighted gene co-expression network analysis (WGCNA) method was used to construct the network, and the main modules and core genes of co-expression were screened according to the results of gene expression and module function enrichment analysis. According to the correlation of gene expression, the network was divided into 19 modules. By analyzing the expression patterns of each module gene, Blue, Salmon and Yellow were identified as the key modules of peony heat response related functions. GO and KEGG functional enrichment analysis was performed on the genes in the three modules and a network diagram was constructed. Based on this, two key genes PsWRKY53 (TRINITY_DN60998_c1_g2, TRINITY_DN71537_c0_g1) and PsHsfB2b (TRINITY_DN56794_c0_g1) were excavated, which may play a key role in the heat shock response of peony. The three co-expression modules and two key genes were helpful to further elucidate the heat resistance mechanism of P. suffruticosa 'Hu Hong'.

Gauging the stress of long-term care nursing assistants using ecological momentary assessment, wearable sensors and end of day reconstruction.

BACKGROUND: Nursing assistants working in long-term care (LTC) often report that their job is stressful. To reduce their work stress, a better understanding of their stress profile is needed. OBJECTIVE: We aimed to pilot test methods to identify and understand stressors that LTC nursing assistants experience. METHODS: We asked each participant to provide wearable sensor/watch data, ecological momentary assessment (EMA) surveys and end of day review data over two eight-hour working shifts. RESULTS: Eight nursing assistants participated. All participants worked in a common continuing care retirement community in Maryland, United States of America. Our stress assessment method revealed 83 stressful events that were classified under 10 categories. Most of the reported events were rated as having a mild to low-moderate intensity. The three most common causes of stress were work demands and pressure (28.35%), heavy workload and staffing (19.69%), and safety issues and COVID-19 concerns (17.32%). We also explored the difference between stress events and intensity among different shifts. Disrespect from residents (22.73%) was the most commonly reported stressor during day shifts. Feeling rushed was the most commonly reported stressor during the evening (22.47%) and the night (38.46%) shifts. CONCLUSIONS: We found stress was commonly reported. Stress intensity conflicted with prior literature, and we explored possible explanations. IMPLICATIONS FOR PRACTICE: We discuss potential implications for these findings, modification of our methods to increase feasibility, the utility of these data collection methods for future work and suggest next steps.

Overlapping Physiologic Signs of Sepsis and Paroxysmal Sympathetic Hyperactivity After Traumatic Brain Injury: Exploring A Clinical Conundrum.

BACKGROUND: Paroxysmal sympathetic hyperactivity (PSH) occurs in a subset of patients with traumatic brain injury (TBI) and is associated with worse outcomes. Sepsis is also associated with worse outcomes after TBI and shares several physiologic features with PSH, potentially creating diagnostic confusion and suboptimal management of each. This is the first study to directly investigate the interaction between PSH and infection using robust diagnostic criteria. METHODS: We performed a retrospective cohort study of patients with TBI admitted to a level I trauma center intensive care unit with hospital length of stay of at least 2 weeks. From January 2016 to July 2018, 77 patients diagnosed with PSH were 1:1 matched by age and Glasgow Coma Scale to 77 patients without PSH. Trauma infectious diseases subspecialists prospectively documented assessments corroborating diagnoses of infection. Extracted data including incidence, timing, classification, and anatomical source of infections were compared according to PSH diagnosis. We also evaluated daily PSH clinical feature severity scores and systemic inflammatory response syndrome (SIRS) criteria and compared values for patients with and without confirmed infection, stratified by PSH diagnosis. RESULTS: During the first 2 weeks of hospitalization, there were no differences in rates of suspected (62%) nor confirmed (48%) infection between patients with PSH and controls. Specific treatments for PSH were initiated on median hospital day 7 and for confirmed infections on median hospital day 8. SIRS criteria could identify infection only in patients who were not diagnosed with PSH. CONCLUSIONS: In the presence of brain injury-induced autonomic nervous system dysregulation, the initiation and continuation of antimicrobial therapy is a challenging clinical decision, as standard physiologic markers of sepsis do not distinguish infected from noninfected patients with PSH, and these entities often present around the same time. Clinicians should be aware that PSH is a potential driver of SIRS, and familiarity with its diagnostic criteria as proposed by the PSH assessment measure is important. Management by a multidisciplinary team attentive to these issues may reduce rates of inappropriate antibiotic usage and misdiagnoses.

A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement.

Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded global healthcare system, which receives approximately 60 million primary care visits and 6 million emergency room visits annually. These systems, developed by clinical psychologists, psychiatrists, and AI researchers, are designed to aid in Cognitive Behavioral Therapy (CBT). The main focus of VMHAs is to provide relevant information to mental health professionals (MHPs) and engage in meaningful conversations to support individuals with mental health conditions. However, certain gaps prevent VMHAs from fully delivering on their promise during active communications. One of the gaps is their inability to explain their decisions to patients and MHPs, making conversations less trustworthy. Additionally, VMHAs can be vulnerable in providing unsafe responses to patient queries, further undermining their reliability. In this review, we assess the current state of VMHAs on the grounds of user-level explainability and safety, a set of desired properties for the broader adoption of VMHAs. This includes the examination of ChatGPT, a conversation agent developed on AI-driven models: GPT3.5 and GPT-4, that has been proposed for use in providing mental health services. By harnessing the collaborative and impactful contributions of AI, natural language processing, and the mental health professionals (MHPs) community, the review identifies opportunities for technological progress in VMHAs to ensure their capabilities include explainable and safe behaviors. It also emphasizes the importance of measures to guarantee that these advancements align with the promise of fostering trustworthy conversations.

The Freeze-Thaw Strength Evolution of Fiber-Reinforced Cement Mortar Based on NMR and Fractal Theory: Considering Porosity and Pore Distribution.

Predicting the strength evolution of fiber-reinforced cement mortar under freeze-thaw cycles plays an important role in engineering stability evaluation. In this study, the microscopic pore distribution characteristics of fiber-reinforced cement mortar were obtained by using nuclear magnetic resonance (NMR) technology. The change trend of T2 spectrum curve and porosity cumulative distribution curve showed that the freeze-thaw resistance of cement mortar increased first and then decreased with the fiber content. The optimal fiber content was approximately 0.5%. By conducting mechanical experiments, it is found that the uniaxial compressive strength (UCS) of the samples exhibited the 'upward convex' evolution trends with freeze-thaw cycles due to cement hydration, and based on fractal theory, the negative correlation between UCS and Dmin was established. Eventually, a freeze-thaw strength prediction model considering both porosity and pore distribution was proposed, which could accurately predict the strength deterioration law of cement-based materials under freeze-thaw conditions.

Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications.

Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.

The limited value of triage vital signs in predicting influenza infection in children aged 5 years and under in the emergency department: A single-center retrospective cross-sectional study.

ABSTRACT: Diagnosing influenza in children aged 5 years and under can be challenging because of their difficulty in verbalizing symptoms. This study aimed to explore the value of the triage heart rate (HR), respiratory rate (RR), and temperature, either alone or when combined with individual symptoms and signs, in predicting influenza infection in this age group.This was a retrospective study covering 4 influenza seasons from 2017 to 2019 in an emergency department (ED) in Hong Kong. We recruited patients ≤5 years of age who had an reverse transcription polymerase chain reaction influenza test within 48 hours of ED presentation. The diagnostic performance of the triage HR, RR, and temperature was evaluated as dichotomized or categorized values with diagnostic odds ratios (DORs) calculated based on different age-appropriate thresholds. Linear discriminant analysis was performed to assess the combined discriminatory effect of age, HR, RR, and temperature as continuous variables.Of 322 patients (median age 26 months), 99 had influenza A and 13 had influenza B infection. For HR and RR dichotomized based on age-appropriate thresholds, the DORs ranged from 1.16 to 1.54 and 0.78 to 1.53, respectively. A triage temperature ≥39.0 °C had the highest DOR (3.32) among different degrees of elevation of temperature. The diagnostic criteria that were based on the presence of fever and cough and/or rhinitis symptoms had a higher DOR compared with the Centers for Disease Control and Prevention influenza-like illness criteria (4.42 vs 2.41). However, combining HR, RR, or temperature with such diagnostic criteria added very little to the diagnostic performance. The linear discriminant analysis model had a high specificity of 92.5%, but the sensitivity (18.3%) was too low for clinical use.Triage HR, RR, and temperature had limited value in the diagnosis of influenza in children ≤5 years of age in the ED. Fever and cough and/or rhinitis symptoms had a better diagnostic performance than the Centers for Disease Control and Prevention influenza-like illness criteria in predicting influenza in this age group.

Shashen maidong decoction: the effect of TNF-α and IL-6 on lung cancer cachexia based on cancer toxicity theory.

OBJECTIVE: To study the curative effect of the traditional Chinese medicine (TCM) Shashen Maidong Decoction in treating lung cancer cachexia based on the cancer toxicity theory, and analyze its influence on patients' serum tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6). METHODS: From January 2018 to January 2019, 104 patients with primary lung cancer cachexia diagnosed and treated in our hospital's oncology department were selected and randomly divided into experimental group and control group, with 52 cases in each group. The control group received routine treatment and nutritional support, while the experimental group received Shashen Maidong Decoction plus. Indexes were compared and analyzed before and after 4 weeks of treatment, including TCM syndrome score, Patient-Generated Subjective Global Assessment (PG-SGA) score, Karnofsky score (KPS), albumin, prealbumin, TNF-α and IL-6 levels. RESULTS: (1) After treatment, both groups' PG-SGA score, KPS score and TCM syndrome score were better than those before treatment (P < 0.01), and the experimental group's PG-SGA score, KPS score and TCM syndrome score were higher than those of the control group (P < 0.01). (2) After treatment, both groups' serum albumin and prealbumin levels were higher than those before treatment (P < 0.05), and the experimental group's prealbumin level was higher than that in the control group (P < 0.05). (3) After treatment, both groups' serum levels of TNF-α and IL-6 were lower than those before treatment (P < 0.05), and the experimental group's levels of TNF-α and IL-6 were lower than those in the control group (P < 0.05). CONCLUSION: Based on the cancer toxicity theory, the application of Shashen Maidong Decoction in treating lung cancer cachexia has definite therapeutic effects and important clinical values. It can effectively alleviate patients' symptoms, improve nutritional status, and reduce body's inflammatory response.

Micro-CT image denoising with an asymmetric perceptual convolutional network.

Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high signal-to-noise image under the requirement of temporal resolution. Therefore, low-dose CT image denoising has attracted considerable attention to improve the image quality of micro-CT while maintaining time resolution. In this paper, an end-to-end asymmetric perceptual convolutional network (APCNet) is proposed to enhance the network's ability to capture and retain image details by improving the convolutional layer and introducing an edge detection layer. Compared with the previously proposed denoising models such as DnCNN, CNN-VGG, and RED-CNN, experiments proved that our proposed method has achieved better results in both numerical indicators and visual perception.

microRNA-145 Inhibition Upregulates SIRT1 and Attenuates Autophagy in a Mouse Model of Lung Ischemia/Reperfusion Injury via NF-κB-dependent Beclin 1.

BACKGROUND: MicroRNA-145 (miR-145) has been shown to play a critical role in ischemia/reperfusion (I/R) injury; however, the expression and function of miR-145 in lung I/R injury have not been reported yet. This study aimed to elucidate the potential effects of miR-145 in lung I/R injury. METHODS: Lung I/R mice models and hypoxia/reoxygenation (H/R) pulmonary microvascular endothelial cell models were established. The expression of miR-145 and sirtuin 1 (SIRT1) was measured with reverse transcription-quantitative polymerase chain reaction and Western blot analysis in mouse lung tissue and cells. Artificial modulation of miR-145 and SIRT1 (downregulation) was done in I/R mice and H/R cells. Additionally, Pao2/FiO2 ratio, wet weight-to-dry weight ratio, and cell apoptosis in mouse lung tissues were determined by blood gas analyzer, electronic balance, and deoxyuridine triphosphate-biotin nick end-labeling assay, respectively. Autophagy marker Beclin 1 and LC3 expression, NF-κB acetylation levels, and autophagy bodies were detected in cell H/R and mouse I/R models by Western blot analysis. pulmonary microvascular endothelial cell apoptosis was detected with flow cytometry. RESULTS: miR-145 was abundantly expressed in the lung tissue of mice and PMVECs following I/R injury. In addition, miR-145 directly targeted SIRT1, which led to significantly decreased Pao2/FiO2 ratio and increased wet weight-to-dry weight ratio, elevated acetylation levels and transcriptional activity of NF-κB, upregulated expressions of tumor necrosis factor-α, interleukins-6, and Beclin 1, autophagy bodies, cell apoptosis, as well as LC3-II/LC3I ratio. CONCLUSIONS: In summary, miR-145 enhances autophagy and aggravates lung I/R injury by promoting NF-κB transcriptional activity via SIRT1 expression.

Performance of a three-level triage scale in live triage encounters in an emergency department in Hong Kong.

BACKGROUND: Despite its continued use in many low-volume emergency departments (EDs), 3-level triage systems have not been extensively studied, especially on live triage cases. We have modified from the Australasian Triage Scale and developed a 3-level triage scale, and sought to evaluate its validity, reliability, and over- and under-triage rates in real patient encounters in our setting. METHOD: This was a cross-sectional study in a single ED with 24,000 attendances per year. At triage, each patient was simultaneously assessed by a triage nurse, an adjudicator (the "criterion standard"), and a study nurse independently. Predictive validity was determined by comparing clinical outcomes, such as hospitalization, across triage levels. The discriminating performance of the triage tool in identifying patients requiring earlier medical attention was determined. Inter-observer reliability between the triage nurse and criterion standard, and across providers were determined using kappa statistics. RESULTS: In total, 453 triage ratings of 151 triage cases, involving 17 ED triage nurses and 57 nurse pairs, were analysed. The proportion of hospital admission significantly increased with a higher triage rating. The performance of the scale in identifying patients requiring earlier medical attention was as follows: sensitivity, 68.2% (95% CI 45.1-86.1%); specificity, 99.2% (95% CI 95.8-100%); positive predictive value, 93.8% (95% CI 67.6-99.1%); and negative predictive value, 94.8% (95% CI 90.8-97.1%). The over-triage and under-triage rates were 0.7% and 4.6%, respectively. Agreement between the triage nurse and criterion standard was substantial (quadratic-weighted kappa = 0.76, 95% CI, 0.60-0.92, p < 0.001), so was the agreement across nurses (quadratic-weighted kappa = 0.81, 95% CI 0.65-0.97, p < 0.001). CONCLUSIONS: The 3-level triage system appears to have good validity and reasonable reliability in a low-volume ED setting. Further studies comparing 3-level and prevailing 5-level triage scales in live triage encounters and different ED settings are warranted.

Injury patterns of mass casualty incidents involving high-speed passenger ferries presenting to accident and emergency departments in Hong Kong: a retrospective review.

BACKGROUND: Accidents involving high-speed passenger ferries have the potential to cause mass-casualty incidents (MCIs), yet there is a lack of relevant studies available to inform hospital disaster preparedness planning. OBJECTIVE: The objective was to study the injury patterns and outcomes of MCI victims involved in high-speed passenger ferry accidents in Hong Kong waters. METHODS: A retrospective study was conducted from 1 January 2005 to 31 December 2015. All MCIs involving high-speed passenger ferries were captured from the Marine Department of Hong Kong. Victims of all age who were sent to the accident and emergency departments (A&Es) of seven public hospitals around Victoria Harbour, including three trauma centres, were identified from electronic disaster registries of the study hospitals. Data on injury patterns and outcomes were extracted from medical records with the Injury Severity Score (ISS) calculated for each victim. The Kruskal-Wallis test was used to compare medians of the ISS across different mechanisms of injury. Multivariable logistic regression was performed to identify independent predictors for major trauma (ISS≥16). RESULTS: During the study period, eight MCIs involving high-speed passenger ferries were reported and 512 victims (median age: 44 years, age range: 2-85 years) were sent to the study hospitals. The A&E triage categories were Cat 1, 3.1%; Cat 2, 4.3%; Cat 3, 19.3%; Cat 4, 72.9%; and Cat 5, 0.4%, respectively. The median ISS was 1.0 (interquartile range: 1.0-2.0). Fourteen victims (2.7%) had an ISS≥16 and age was the only independent predictor for major trauma (OR 1.06, p = 0.025, 95% CI 1.01-1.11). Trauma call was activated at A&E for 11 victims. In total, 100 victims (19.5%) were admitted to the study hospitals, including 19 (3.5%) and 15 (2.9%) who required surgery and intensive care unit stay, respectively. Eleven victims (2.1%) died, mostly due to drowning. CONCLUSION: MCIs involving high-speed passenger ferries can result in a sudden surge in demand for both A&E and in-patient care, though the majority of victims may have minor injuries. Better access to lifejackets and mandatory seatbelt use may help to reduce injuries and deaths.

Predicting tachycardia as a surrogate for instability in the intensive care unit.

Tachycardia is a strong though non-specific marker of cardiovascular stress that proceeds hemodynamic instability. We designed a predictive model of tachycardia using multi-granular intensive care unit (ICU) data by creating a risk score and dynamic trajectory. A subset of clinical and numerical signals were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A tachycardia episode was defined as heart rate ≥ 130/min lasting for ≥ 5 min, with ≥ 10% density. Regularized logistic regression (LR) and random forest (RF) classifiers were trained to create a risk score for upcoming tachycardia. Three different risk score models were compared for tachycardia and control (non-tachycardia) groups. Risk trajectory was generated from time windows moving away at 1 min increments from the tachycardia episode. Trajectories were computed over 3 hours leading up to the episode for three different models. From 2809 subjects, 787 tachycardia episodes and 707 control periods were identified. Patients with tachycardia had increased vasopressor support, longer ICU stay, and increased ICU mortality than controls. In model evaluation, RF was slightly superior to LR, which accuracy ranged from 0.847 to 0.782, with area under the curve from 0.921 to 0.842. Risk trajectory analysis showed average risks for tachycardia group evolved to 0.78 prior to the tachycardia episodes, while control group risks remained < 0.3. Among the three models, the internal control model demonstrated evolving trajectory approximately 75 min before tachycardia episode. Clinically relevant tachycardia episodes can be predicted from vital sign time series using machine learning algorithms.

A call to alarms: Current state and future directions in the battle against alarm fatigue.

Research demonstrates that the majority of alarms derived from continuous bedside monitoring devices are non-actionable. This avalanche of unreliable alerts causes clinicians to experience sensory overload when attempting to sort real from false alarms, causing desensitization and alarm fatigue, which in turn leads to adverse events when true instability is neither recognized nor attended to despite the alarm. The scope of the problem of alarm fatigue is broad, and its contributing mechanisms are numerous. Current and future approaches to defining and reacting to actionable and non-actionable alarms are being developed and investigated, but challenges in impacting alarm modalities, sensitivity and specificity, and clinical activity in order to reduce alarm fatigue and adverse events remain. A multi-faceted approach involving clinicians, computer scientists, industry, and regulatory agencies is needed to battle alarm fatigue.

Motorizing and Optimizing Ultrasound Strain Elastography for Detection of Intrauterine Growth Restriction Pregnancies.

Intrauterine growth restriction is a prevalent disease in pregnancy in which placental insufficiency leads to 5 to 10 times higher mortality and lifelong morbidities. The current detection rate is poor, and recently, ultrasound strain elastography (USEL) was proposed as a new diagnostic technique. Currently, placental USEL uses maternal subcutaneous fat as the reference layer, but this is not ideal as fat tissue stiffness can vary widely between subjects. Current USEL also uses manual palpation, and under different compression depths and rates, viscoelastic tissues such as placenta can yield different stiffness results. In the study described here, we strove to improve placental USEL by (i) using an external polymeric pad of known stiffness as the reference layer and (ii) adopting motorized control of the transducer during USEL to standardize palpation motion. Results indicated that motorized USEL reduced measurement variability by 67% compared with freehand USEL. Satisfactory and statistically significant correlations between USEL measurements and mechanical testing validation results were obtained for our new USEL protocol. Placental tissues were found to be non-linear and viscoelastic in nature and, thus, differed in stiffness at different compression rates and depths. Our study also revealed that there was a specific compression depth and rate during USEL that provided better correlation to mechanical testing, and should be considered in clinical placental USEL.

Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change.

Cardiorespiratory instability (CRI) in monitored step-down unit (SDU) patients has a variety of etiologies, and likely manifests in patterns of vital signs (VS) changes. We explored use of clustering techniques to identify patterns in the initial CRI epoch (CRI1; first exceedances of VS beyond stability thresholds after SDU admission) of unstable patients, and inter-cluster differences in admission characteristics and outcomes. Continuous noninvasive monitoring of heart rate (HR), respiratory rate (RR), and pulse oximetry (SpO2) were sampled at 1/20 Hz. We identified CRI1 in 165 patients, employed hierarchical and k-means clustering, tested several clustering solutions, used 10-fold cross validation to establish the best solution and assessed inter-cluster differences in admission characteristics and outcomes. Three clusters (C) were derived: C1) normal/high HR and RR, normal SpO2 (n = 30); C2) normal HR and RR, low SpO2 (n = 103); and C3) low/normal HR, low RR and normal SpO2 (n = 32). Clusters were significantly different based on age (p < 0.001; older patients in C2), number of comorbidities (p = 0.008; more C2 patients had ≥ 2) and hospital length of stay (p = 0.006; C1 patients stayed longer). There were no between-cluster differences in SDU length of stay, or mortality. Three different clusters of VS presentations for CRI1 were identified. Clusters varied on age, number of comorbidities and hospital length of stay. Future study is needed to determine if there are common physiologic underpinnings of VS clusters which might inform clinical decision-making when CRI first manifests.

Risk for Cardiorespiratory Instability Following Transfer to a Monitored Step-Down Unit.

BACKGROUND: Hospitalized patients who develop at least one instance of cardiorespiratory instability (CRI) have poorer outcomes. We sought to describe the admission characteristics, drivers, and time to onset of initial CRI events in monitored step-down unit (SDU) patients. METHODS: Admission characteristics and continuous monitoring data (frequency 1/20 Hz) were recorded in 307 subjects. Vital sign deviations beyond local instability trigger threshold criteria, with a tolerance of 40 s and cumulative duration of 4 of 5 min, were classified as CRI events. The CRI driver was defined as the first vital sign to cross a threshold and meet persistence criteria. Time to onset of initial CRI was the number of days from SDU admission to initial CRI, and duration was length of the initial CRI epoch. RESULTS: Subjects transferred to the SDU from units with higher monitoring capability were more likely to develop CRI (CRI n = 133 [44%] vs no CRI n = 174 [31%] P = .042). Time to onset varied according to the CRI driver. Subjects with at least one CRI event had a longer hospital stay (CRI 11.3 ± 10.2 d vs no CRI 7.8 ± 9.2 d, P < .001) and SDU stay (CRI 6.1 ± 4.9 d vs no CRI 3.5 ± 2.9 d, P < .001). First events were more often due to SpO2 , whereas breathing frequency was the most common driver of all CRI. CONCLUSIONS: Initial CRI most commonly occurred due to SpO2 and was associated with prolonged SDU and hospital stay. Findings suggest the need for clinicians to more closely monitor SDU patients transferred from an ICU and parameters (SpO2 , breathing frequency) that more commonly precede CRI events.

Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms.

RATIONALE: Cardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored. OBJECTIVES: To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients. METHODS: Using a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after step-down unit admission, and again during the 4 hours immediately before the CRI event, between cases (ever had a CRI) and control subjects (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk. MEASUREMENTS AND MAIN RESULTS: Estimated risk was significantly higher for cases (918) than control subjects (1,053; P ≤ 0.001) during the initial 4-hour stable periods. Among cases, the aggregated nonpersonalized risk trend increased 2 hours before the CRI, whereas the personalized risk trend became significantly different from control subjects 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for nonpersonalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset). CONCLUSIONS: Insights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI, taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful in the dynamic allocation of technological and clinical personnel resources in acute care hospitals.

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

OBJECTIVE: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. DESIGN: Observational cohort study. SETTING: Twenty-four-bed trauma step-down unit. PATIENTS: Two thousand one hundred fifty-three patients. INTERVENTION: Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. MEASUREMENTS AND MAIN RESULTS: The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. CONCLUSIONS: Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).