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.

Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19.

Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the Pao2 to the Fio2 (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously (ratio of the Spo2 to the Fio2 [S/F ratio]), but it is affected by skin color and occult hypoxemia can occur in Black patients. Oxygen dissociation curves allow noninvasive estimation of P/F ratios (ePFRs) but remain unproven. OBJECTIVES: Measure overt and occult hypoxemia using ePFR. DESIGN SETTING AND PARTICIPANTS: We retrospectively studied COVID-19 hospital encounters (n = 5,319) at two academic centers (University of Virginia [UVA] and Emory University). MAIN OUTCOMES AND MEASURES: We measured primary outcomes (death or ICU transfer within 24 hr), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score [NEWS] and Sequential Organ Failure Assessment [SOFA]). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AORs) and area under the receiver operating characteristic curves (AUROCs). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test. RESULTS: Overt hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p < 0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (0.70 [both sites]) or SOFA (0.68 [UVA]; 0.65 [Emory]) and similar to S/F ratio (0.76 [UVA]; 0.70 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p < 0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory]; p < 0.01). CONCLUSIONS AND RELEVANCE: The ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models. By accounting for biased oximetry as well as clinicians' real-time responses to it (supplemental oxygen adjustment), ePFRs may reveal racial disparities attributable to occult hypoxemia.

Overt and occult hypoxemia in patients hospitalized with novel coronavirus disease 2019.

Background: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously, but occult hypoxemia can occur in Black patients because the technique is affected by skin color. Oxygen dissociation curves allow non-invasive estimation of P/F ratios (ePFR) but this approach remains unproven. Research Question: Can ePFRs measure overt and occult hypoxemia? Study Design and methods: We retrospectively studied COVID-19 hospital encounters (n=5319) at two academic centers (University of Virginia [UVA] and Emory University). We measured primary outcomes (death or ICU transfer within 24 hours), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score (NEWS) and Sepsis-3). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AOR) and area under receiver operating characteristics curves (AUROC). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test. Results: Overt hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p<0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (AUROC: 0.70 [UVA]; 0.70 [Emory]) or Sepsis-3 (AUROC: 0.68 [UVA]; 0.65 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p<0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory], p<0.01). Interpretation: The ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models like NEWS and Sepsis-3. By accounting for biased oximetry as well as clinicians’ real-time responses to it (supplemental oxygen adjustment), ePFRs may enable statistical modelling of racial disparities in outcomes attributable to occult hypoxemia.

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation.

OBJECTIVES: We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients. DESIGN: Retrospective analysis leading to model development and validation. SETTING: All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record. PATIENTS: Eleven thousand eight hundred forty-seven ICU patient admissions. INTERVENTIONS: The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model. MEASUREMENTS AND MAIN RESULTS: Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81). CONCLUSIONS: We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

Autism risk in neonatal intensive care unit patients associated with novel heart rate patterns.

BACKGROUND: Neonatal intensive care unit (NICU) patients are at increased risk for autism spectrum disorder (ASD). Autonomic nervous system aberrancy has been described in children with ASD, and we aimed to identify heart rate (HR) patterns in NICU patients associated with eventual ASD diagnosis. METHODS: This retrospective cohort study included NICU patients from 2009 to 2016 with archived HR data and follow-up beyond age 3 years. Medical records provided clinical variables and ASD diagnosis. HR data were compared in infants with and without ASD. RESULTS: Of the 2371 patients, 88 had ASD, and 689,016 h of data were analyzed. HR skewness (HRskw) was significantly different between ASD and control infants. Preterm infants at early postmenstrual ages (PMAs) had negative HRskw reflecting decelerations, which increased with maturation. From 34 to 42 weeks PMA, positive HRskw toward accelerations was higher in males with ASD. In 931 males with at least 4 days of HR data, overall ASD prevalence was 5%, whereas 11% in the top 5th HRskw percentile had ASD. CONCLUSION: High HRskw in NICU males, perhaps representing autonomic imbalance, was associated with increased ASD risk. Further study is needed to determine whether HR analysis identifies highest-risk infants who might benefit from earlier screening and therapies. IMPACT: In a large retrospective single-center cohort of NICU patients, we found that high positive skewness of heart rate toward more accelerations was significantly associated with increased risk of eventual autism spectrum disorder diagnosis in male infants but not in females. Existing literature describes differences in heart rate characteristics in children, adolescents, and adults with autism spectrum disorders, but the finding from our study in NICU infants is novel. Heart rate analysis during the NICU stay might identify, among an inherently high-risk population, those infants with especially high risk of ASD who might benefit from earlier screening and therapies.

The Relationship Between Acuity of Organ Failure and Predictive Validity of Sepsis-3 Criteria.

The Sepsis-3 taskforce defined sepsis as suspicion of infection and an acute rise in the Sequential Organ Failure Assessment score by 2 points over the preinfection baseline. Sepsis-3 studies, though, have not distinguished between acute and chronic organ failure, and may not accurately reflect the epidemiology, natural history, or impact of sepsis. Our objective was to determine the extent to which the predictive validity of Sepsis-3 is attributable to chronic rather than acute organ failure. DESIGN: Retrospective cohort study. SETTING: General medicine inpatient service at a tertiary teaching hospital. PATIENTS: A total of 3,755 adult medical acute-care encounters (1,864 confirmed acute infections) over 1 year. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We measured the total Sequential Organ Failure Assessment score at the onset of infection and separated its components (baseline and acute rise) using case-by-case chart reviews. We compared the predictive validities of acuity-focused (acute rise in Sequential Organ Failure Assessment ≥ 2) and conventional (total Sequential Organ Failure Assessment ≥ 2) implementations of Sepsis-3 criteria. Measures of predictive validity were change in the rate of outcomes and change in the area under receiver operating characteristic curves after adding sepsis criteria to multivariate logistic regression models of baseline risk (age, sex, race, and Charlson comorbidity index). Outcomes were inhospital mortality (primary) and ICU transfer or inhospital mortality (secondary). Acuity-focused implementations of Sepsis-3 were associated with neither a change in mortality (2.2% vs 1.2%; p = 0.18) nor a rise in area under receiver operating characteristic curves compared with baseline models (0.67 vs 0.66; p = 0.75). In contrast, conventional implementations were associated with a six-fold change in mortality (2.4% vs 0.4%; p = 0.01) and a rise in area under receiver operating characteristic curves compared with baseline models (0.70 vs 0.66; p = 0.04). Results were similar for the secondary outcome. CONCLUSIONS: The evaluation of the validity of organ dysfunction-based clinical sepsis criteria is prone to bias, because acute organ dysfunction consequent to infection is difficult to separate from preexisting organ failure in large retrospective cohorts.

Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart's rhythm.

To demonstrate how heart rate fragmentation gives novel insights into non-autonomic mechanisms of beat-to-beat variability in cycle length, and predicts survival of cardiology clinic patients, over and above traditional clinical risk factors and measures of heart rate variability. Approach: We studied 2893 patients seen by cardiologists with clinical data including 24-hour Holter monitoring. Novel measures of heart rate fragmentation alongside canonical time and frequency domain measures of heart rate variability, as well as an existing local dynamics score were calculated. A proportional hazards model was utilized to relate the results to survival. Main results: The novel heart rate fragmentation measures were validated and characterized with respect to the effects of age, ectopy and atrial fibrillation. Correlations between parameters were determined. Critically, heart rate fragmentation results could not be accounted for by undersampling respiratory sinus arrhythmia. Increased heart rate fragmentation was associated with poorer survival (p ≪ 0.01 in the univariate model). In multivariable analyses, increased heart rate fragmentation and more abnormal local dynamics (p 0.045), along with increased clinical risk factors (age (p ≪ 0.01), tobacco use (p ≪ 0.01) and history of heart failure (p 0.019)) and lower low- to high-frequency ratio (p 0.022) were all independent predictors of 2-year mortality. Significance: Analysis of continuous ECG data with heart rate fragmentation indices yields information regarding non-autonomic control of beat-to-beat variability in cycle length that is independent of and additive to established parameters for investigating heart rate variability, and predicts mortality in concert with measures of local dynamics, frequency content of heart rate, and clinical risk factors.