Performance of a Risk Analytic Tool (Index of Tissue Oxygen Delivery "IDO2") in Pediatric Cardiac Intensive Care Unit of a Developing Country

Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world.

OBJECTIVE

To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making.

METHODS

A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People's Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram.

RESULTS

Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858-0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701-0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818-0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients.

CONCLUSION

SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.

A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients

BACKGROUND

Postoperative pediatric congenital heart patients are predisposed to develop low-cardiac output syndrome. Serum lactate (lactic acid [LA]) is a well-defined marker of inadequate systemic oxygen delivery.

OBJECTIVES

We hypothesized that a near real-time risk index calculated by a noninvasive predictive analytics algorithm predicts elevated LA in pediatric patients admitted to a cardiac ICU (CICU).

DERIVATION COHORT

Ten tertiary CICUs in the United States and Pakistan.

VALIDATION COHORT

Retrospective observational study performed to validate a hyperlactatemia (HLA) index using T3 platform data (Etiometry, Boston, MA) from pediatric patients less than or equal to 12 years of age admitted to CICU (n = 3,496) from January 1, 2018, to December 31, 2020. Patients lacking required data for module or LA measurements were excluded.

PREDICTION MODEL

Physiologic algorithm used to calculate an HLA index that incorporates physiologic data from patients in a CICU. The algorithm uses Bayes' theorem to interpret newly acquired data in a near real-time manner given its own previous assessment of the physiologic state of the patient.

RESULTS

A total of 58,168 LA measurements were obtained from 3,496 patients included in a validation dataset. HLA was defined as LA level greater than 4 mmol/L. Using receiver operating characteristic analysis and a complete dataset, the HLA index predicted HLA with high sensitivity and specificity (area under the curve 0.95). As the index value increased, the likelihood of having higher LA increased (p < 0.01). In the validation dataset, the relative risk of having LA greater than 4 mmol/L when the HLA index is less than 1 is 0.07 (95% CI: 0.06-0.08), and the relative risk of having LA less than 4 mmol/L when the HLA index greater than 99 is 0.13 (95% CI, 0.12-0.14).

CONCLUSIONS

These results validate the capacity of the HLA index. This novel index can provide a noninvasive prediction of elevated LA. The HLA index showed strong positive association with elevated LA levels, potentially providing bedside clinicians with an early, noninvasive warning of impaired cardiac output and oxygen delivery. Prospective studies are required to analyze the effect of this index on clinical decision-making and outcomes in pediatric population.

The Inadequate Oxygen Delivery Index and Its Correlation with Venous Saturation in the Pediatric Cardiac Intensive Care Unit

Continuous monitoring software, T3, has an integrated index called the inadequate oxygen delivery index 50% (IDO2-50) which displays a probability that the mixed venous saturation is below a user-selected threshold of 30-50%. The primary aim of this study was to determine the correlation of the IDO2-50 with a measured venous saturation. The secondary aim of this study was to characterize the hemodynamic factors that contributed to the IDO2-50. This single-center, retrospective study aimed to characterize the correlation between IDO2-50 and inferior vena cava (IVC) saturation. A Bayesian Pearson correlation was conducted to assess the correlation between the collected variables of interest, with a particular interest in the correlation between the IDO2-50 and the IVC saturation. Receiver operator curve (ROC) analysis to assess the ability of the IDO2-50 to identify when the venous saturation was less than 50%. Bayesian linear regression was done with the IDO2-50 (dependent variable) and other independent variables. A total of 113 datasets were collected across 15 unique patients. IDO2-50 had moderate correlation with the IVC saturation (correlation coefficient - 0.569). The IDO2-50 had a weak but significant correlation with cerebral near-infrared spectroscopy (NIRS) values, a weak but significant correlation with heart rate, and a moderate and significant correlation with arterial saturation. ROC analysis demonstrated that the IDO2-50 had a good ability to identify a venous saturation below 50%, with an area under the curve of 0.797, cutoff point of 24.5 with a sensitivity of 81%, specificity of 66%, positive predictive value of 44%, and negative predictive value of 91%. Bayesian linear regression analysis yielded the following model: 237.82 + (1.18 × age in months) - (3.31 × arterial saturation) - (1.92 × cerebral NIRS) + (0.84 × heart rate). The IDO2 index has moderate correlation with IVC saturation. It has good sensitive and negative predictive value. Cerebral NIRS does appear to correlate better with the underlying venous saturation than the IDO2 index.