Discovery of a Unique Set of Dog-Seroreactive Coccidioides Proteins Using Nucleic Acid Programmable Protein Array.

Valley Fever (VF), caused by fungi in the genus Coccidioides, is a prevalent disease in southwestern and western parts of the United States that affects both humans and animals, such as dogs. Although the immune responses to infection with Coccidioides spp. are not fully characterized, antibody-detection assays are used in conjunction with clinical presentation and radiologic findings to aid in the diagnosis of VF. These assays often use Complement Fixation (CF) and Tube Precipitin (TP) antigens as the main targets of IgG and IgM reactivity, respectively. Our group previously reported evidence of over 800 genes expressed at the protein level in C. posadasii. However, antibody reactivity to the majority of these proteins has never been explored. Using a new, high-throughput screening technology, the Nucleic Acid Programmable Protein Array (NAPPA), we screened serum specimens from dogs against 708 of these previously identified proteins for IgG reactivity. Serum from three separate groups of dogs was analyzed and revealed a small panel of proteins to be further characterized for immuno-reactivity. In addition to CF/CTS1 antigen, sera from most infected dogs showed antibody reactivity to endo-1,3-betaglucanase, peroxisomal matrix protein, and another novel reactive protein, CPSG_05795. These antigens may provide additional targets to aid in antibody-based diagnostics.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.

Objective.Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.Approach. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).Main Results.The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Infants Born Extremely Preterm.

OBJECTIVE: The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation. STUDY DESIGN: This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics). RESULTS: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783. CONCLUSION: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Comparative proteomic analysis of tail regeneration in the green anole lizard, Anolis carolinensis.

As amniote vertebrates, lizards are the most closely related organisms to humans capable of appendage regeneration. Lizards can autotomize, or release their tails as a means of predator evasion, and subsequently regenerate a functional replacement. Green anoles (Anolis carolinensis) can regenerate their tails through a process that involves differential expression of hundreds of genes, which has previously been analyzed by transcriptomic and microRNA analysis. To investigate protein expression in regenerating tissue, we performed whole proteomic analysis of regenerating tail tip and base. This is the first proteomic data set available for any anole lizard. We identified a total of 2,646 proteins - 976 proteins only in the regenerating tail base, 796 only in the tail tip, and 874 in both tip and base. For over 90% of these proteins in these tissues, we were able to assign a clear orthology to gene models in either the Ensembl or NCBI databases. For 13 proteins in the tail base, 9 proteins in the tail tip, and 10 proteins in both regions, the gene model in Ensembl and NCBI matched an uncharacterized protein, confirming that these predictions are present in the proteome. Ontology and pathways analysis of proteins expressed in the regenerating tail base identified categories including actin filament-based process, ncRNA metabolism, regulation of phosphatase activity, small GTPase mediated signal transduction, and cellular component organization or biogenesis. Analysis of proteins expressed in the tail tip identified categories including regulation of organelle organization, regulation of protein localization, ubiquitin-dependent protein catabolism, small GTPase mediated signal transduction, morphogenesis of epithelium, and regulation of biological quality. These proteomic findings confirm pathways and gene families activated in tail regeneration in the green anole as well as identify uncharacterized proteins whose role in regrowth remains to be revealed. This study demonstrates the insights that are possible from the integration of proteomic and transcriptomic data in tail regrowth in the green anole, with potentially broader application to studies in other regenerative models.

Two lateral flow assays for detection of anti-coccidioidal antibodies show similar performance to immunodiffusion in dogs with coccidioidomycosis.

OBJECTIVE: To compare 2 point-of-care lateral flow assays (LFAs) with immunodiffusion (ID) IgG results for anti-coccidioidal antibody detection in dogs with coccidioidomycosis. A further aim was to compare the quantifiable output of 1 of the LFAs to ID antibody titers. SAMPLE: Serum banked from 73 client-owned dogs diagnosed with pulmonary or disseminated coccidioidomycosis. METHODS: ID was used to determine antibody presence and titer against a coccidioidal antigen preparation. All sera were subsequently tested on an LFA based on recombinant chitinase 1 (CTS1) and the commercially available sona LFA. LFA results were analyzed and compared to ID IgG results and clinical diagnosis. RESULTS: All assays showed similar sensitivities in detecting anti-coccidioidal antibodies (83.6% to 89.0%). When compared with ID IgG, the CTS1 LFA had a positive percent agreement of 100%, while the sona LFA had a positive percent agreement of 91.4%. Since the CTS1 LFA is semiquantitative, we were able to compare test line densities with ID titers and found a strong correlation between the 2 assays (Spearman ρ = 0.82). CLINICAL RELEVANCE: This is the first side-by-side evaluation of a commercially available LFA (sona) and a newer more rapid anti-CTS1 antibody LFA using serum from dogs with coccidioidomycosis. Both LFAs tested have similar sensitivity to ID IgG results. The CTS1 LFA can be read after 10 minutes and is semiquantitative, while the sona LFA is read after 30 minutes, and the results are subject to interpretation. Accurate and fast detection of anti-coccidioidal antibodies allows clinicians to initiate appropriate treatment without diagnostic delay.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Extremely Preterm Infants.

Objectives: Detection of changes in cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, may facilitate earlier detection of sepsis. Our objective was to examine the association of cardiorespiratory events with late-onset sepsis for extremely preterm infants (<29 weeks' gestational age (GA)) on versus off invasive mechanical ventilation. Study Design: Retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in five level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean GA 26.4w, SD 1.71). Monitoring data were available and analyzed for 719 infants (47,512 patient-days), of whom 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72h after birth and ≥5d antibiotics). Results: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer IH80 events and more bradycardia events before sepsis. IH events were associated with higher sepsis risk, but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model predicted sepsis with an AUC of 0.783. Conclusion: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.

Objective: Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 90%. Main Results: The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

SETD2 loss in renal epithelial cells drives epithelial-to-mesenchymal transition in a TGF-ß-independent manner.

Histone-lysine N-methyltransferase SETD2 (SETD2), the sole histone methyltransferase that catalyzes trimethylation of lysine 36 on histone H3 (H3K36me3), is often mutated in clear cell renal cell carcinoma (ccRCC). SETD2 mutation and/or loss of H3K36me3 is linked to metastasis and poor outcome in ccRCC patients. Epithelial-to-mesenchymal transition (EMT) is a major pathway that drives invasion and metastasis in various cancer types. Here, using novel kidney epithelial cell lines isogenic for SETD2, we discovered that SETD2 inactivation drives EMT and promotes migration, invasion, and stemness in a transforming growth factor-beta-independent manner. This newly identified EMT program is triggered in part through secreted factors, including cytokines and growth factors, and through transcriptional reprogramming. RNA-seq and assay for transposase-accessible chromatin sequencing uncovered key transcription factors upregulated upon SETD2 loss, including SOX2, POU2F2 (OCT2), and PRRX1, that could individually drive EMT and stemness phenotypes in SETD2 wild-type (WT) cells. Public expression data from SETD2 WT/mutant ccRCC support the EMT transcriptional signatures derived from cell line models. In summary, our studies reveal that SETD2 is a key regulator of EMT phenotypes through cell-intrinsic and cell-extrinsic mechanisms that help explain the association between SETD2 loss and ccRCC metastasis.

Development of tandem antigen capture ELISAs measuring QSOX1 isoforms in plasma and serum.

QSOX1 is a sulfhydryl oxidase that has been identified as a potential biomarker in multiple cancer types as well as acute decompensated heart failure. Three anti-QSOX1 monoclonal antibodies (mAbs) were generated: 2F1, 3A10, and 56-3. MAbs 2F1 and 3A10 were generated against the short isoform of recombinant QSOX1 (rQSOX1-S), and mAb 56-3 was generated against a peptide (NEQEQPLGQWHLS) from the long isoform of QSOX1 (QSOX1-L). Using these mAbs, tandem antigen capture ELISAs were developed to quantify both short and long isoforms of QSOX1 (Total QSOX1 ELISA) and QSOX1-L (QSOX1-L ELISA) in serum and plasma samples. The Total QSOX1 ELISA pairs mAbs 2F1 and 3A10 and has a limit of detection of 109.5 pM, while the QSOX1-L ELISA pairs mAbs 2F1 and 56-3 and has a limit of detection of 10 pM. The levels of total QSOX1 and QSOX1-L were measured in a cohort of paired sera and plasma from 61 donors ≥40 years old and 15 donors <40 years old. No difference in QSOX1 levels was detected between QSOX1-L and QSOX1-S in serum, but the mean concentration of QSOX1-L was found to be 3.21 nM in serum and 5.63 nM in plasma (**p = 0.006). Our tandem ELISAs demonstrate the wide range of concentrations of QSOX1-L and QSOX1-S among individual serum and plasma samples. Since the epitope of mAb 2F1 was mapped to the first CxxC motif at residues C70 and C73 and mAb 56-3 was generated against NEQEQPLGQWHLS in QSOX1-L, our findings support previous research which suggested that QSOX1-L is secreted from cells despite a putative transmembrane domain. The ELISAs reported here may be a useful tool for investigating QSOX1 isoforms as potential biomarkers in cancer and/or heart failure.

Maturation of cardioventilatory physiological trajectories in extremely preterm infants.

BACKGROUND: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life. METHODS: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation. RESULTS: Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing. CONCLUSIONS: Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. IMPACT: Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms.

Development of a rapid lateral flow assay for detection of anti-coccidioidal antibodies.

Coccidioides spp. are dimorphic fungi that are capable of infecting human and non-human mammals and can cause diverse manifestations of coccidioidomycosis or Valley fever (VF). In combination with clinical symptoms and radiographic findings, antibody-based diagnostic tests are often used to diagnose and monitor patients with VF. Chitinase 1 (CTS1) has previously been identified as the seroreactive antigen used in these diagnostic assays to detect anticoccidial IgG. Here, an indirect enzyme-linked immunosorbent assay to detect IgG to CTS1 demonstrated 165 of 178 (92.7%) patients with a positive result by immunodiffusion (ID) and/or complement fixation (CF) had antibodies to the single antigen CTS1. We then developed a rapid antibody lateral flow assay (LFA) to detect anti-CTS1 antibodies. Out of 143 samples tested, the LFA showed 92.9% positive percent agreement [95% confidence interval (CI), 84.3%-96.9%] and 97.7% negative percent agreement (95% CI, 87.9%-99.6%) with ID and CF assays. Serum or plasma from canines, macaques, and dolphins was also tested by the CTS1 LFA. Test line densities of the CTS1 LFA correlated in a linear manner with the reported CF and ID titers for human and non-human samples, respectively. This 10-min point-of-care test for the rapid detection of anti-coccidioidal antibodies could help to inform healthcare providers in real-time, potentially improving the efficiency of healthcare delivery.

Generation and characterization of a monoclonal antibody that binds to Galectin-1.

Galectin-1 is a ß-galactoside-binding lectin that has been implicated as a suppressive molecule in cancer and autoimmune diseases. Gal-1 has known immunomodulatory activity and was found to be expressed on regulatory T cells, leading to the potential for targeted immunotherapies. Anti-Gal-1 monoclonal antibodies were generated in this study using classical hybridoma techniques. MAb 6F3 was found to bind to Gal-1 by Western blot and ELISA. Flow cytometry was used to determine cell surface and intracellular binding of mAb 6F3 to Gal-1 in PBMC-derived Tregs and tumor cells, including Treg-like cell lines. These results suggest mAb 6F3 may be used to further study Gal-1 protein expression and function.

Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants.

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O2/support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O2/support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Volatile Metabolites in Lavage Fluid Are Correlated with Cytokine Production in a Valley Fever Murine Model.

Coccidioides immitis and Coccidioides posadasii are soil-dwelling fungi of arid regions in North and South America that are responsible for Valley fever (coccidioidomycosis). Forty percent of patients with Valley fever exhibit symptoms ranging from mild, self-limiting respiratory infections to severe, life-threatening pneumonia that requires treatment. Misdiagnosis as bacterial pneumonia commonly occurs in symptomatic Valley fever cases, resulting in inappropriate treatment with antibiotics, increased medical costs, and delay in diagnosis. In this proof-of-concept study, we explored the feasibility of developing breath-based diagnostics for Valley fever using a murine lung infection model. To investigate potential volatile biomarkers of Valley fever that arise from host−pathogen interactions, we infected C57BL/6J mice with C. immitis RS (n = 6), C. posadasii Silveira (n = 6), or phosphate-buffered saline (n = 4) via intranasal inoculation. We measured fungal dissemination and collected bronchoalveolar lavage fluid (BALF) for cytokine profiling and for untargeted volatile metabolomics via solid-phase microextraction (SPME) and two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). We identified 36 volatile organic compounds (VOCs) that were significantly correlated (p < 0.05) with cytokine abundance. These 36 VOCs clustered mice by their cytokine production and were also able to separate mice with moderate-to-high cytokine production by infection strain. The data presented here show that Coccidioides and/or the host produce volatile metabolites that may yield biomarkers for a Valley fever breath test that can detect coccidioidal infection and provide clinically relevant information on primary pulmonary disease severity.

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.

BACKGROUND: Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve sepsis risk prediction over HR or demographics alone. METHODS: We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models. RESULTS: Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO2 model performed better than models using either HR or SpO2 alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance. CONCLUSIONS: Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction. IMPACT: Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO2, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

A novel predictive analytics score reflecting accumulating disease burden-an investigation of the cumulative CoMET score.

OBJECTIVE: Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring to deliver an instantaneous prediction of patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay. APPROACH: We have expanded on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCoMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient. MAIN RESULTS: We have shown that higher cCoMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death. SIGNIFICANCE: We have shown that the inclusion of all repeated measures of risk estimation performed throughout a patients hospital stay adds information to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing.

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study.

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.

Clinical Laboratory Utility of a Humanized Antibody in Commercially Available Enzyme Immunoassays for Coccidioidomycosis.

Coccidioidomycosis, also called valley fever (VF), is a fungal infection with endemicity in desert regions of the western United States as well as certain arid regions of Central and South America. Laboratory-based diagnosis of VF often relies on the composite results from three serologic-based diagnostics, complement fixation, immunodiffusion, and enzyme immunoassay (EIA). EIA is commonly performed in clinical laboratories because results can be obtained in a few hours. Two commercially available EIAs, IMMY clarus Coccidioides antibody and Meridian Premier Coccidioides, look for the presence of anticoccidioidal IgG and IgM in patient sera that are diluted 1:441. Per regulatory requirements, this dilution step must be verified with a dilution step control despite not being provided as a reagent in either FDA-approved EIA kit. Therefore, clinical laboratories collect and reuse patient sera in subsequent tests that had a positive result in a previous test. This is a nonstandard process, reinforcing the need for a consistent and reliable dilution control. Here, we evaluate the performance of a humanized IgG and IgM antibody as a dilution control in both EIA kits. Both humanized IgG and IgM work well in each EIA and meet the appropriate threshold for positivity. IMPORTANCE In southwestern and western regions of the United States, at least half a million diagnostic tests for coccidioidomycosis (valley fever) are run annually. Enzyme immunoassays (EIAs) are blood tests which require precise dilution of patient serum prior to testing. To ensure patient serum is properly diluted, there is a regulatory requirement to ensure the dilution step is accurate. Two FDA-approved EIAs used to aid in the diagnosis of coccidioidomycosis do not contain controls for this dilution step, leaving clinical laboratories with the only option of using previously positive patient sera, which may not react in a reliable or predictable manner. Here, we evaluate a humanized monoclonal antibody against a coccidioidal antigen and its utility as a dilution control in both available commercial EIAs. The use of a humanized monoclonal antibody provides a standardized and well-characterized dilution control for use in serological assays that aid in diagnosis of coccidioidomycosis.