A Predictive Model of Reintubation after Cardiac Surgery Using the Electronic Health Record

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation

Background

Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care.

Objective

To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale).

Methods

We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort.

Results

A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803).

Conclusions

We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study

STUDY OBJECTIVE

Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.

DESIGN

We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.

SETTING

Three academic medical centers in the United States.

PATIENTS

Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.

INTERVENTIONS

Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.

MEASUREMENTS

Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).

MAIN RESULTS

The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.

CONCLUSIONS

Future work is needed to explore how to optimize models before local implementation.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

OBJECTIVE

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

METHODS

Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

RESULTS

SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

CONCLUSION

We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Protocol for a randomised controlled trial: reducing reintubation among high-risk cardiac surgery patients with high-flow nasal cannula (I-CAN)

INTRODUCTION

Heated, humidified, high-flow nasal cannula oxygen therapy has been used as a therapy for hypoxic respiratory failure in numerous clinical settings. To date, limited data exist to guide appropriate use following cardiac surgery, particularly among patients at risk for experiencing reintubation. We hypothesised that postextubation treatment with high-flow nasal cannula would decrease the all-cause reintubation rate within the 48 hours following initial extubation, compared with usual care.

METHODS AND ANALYSIS

Adult patients undergoing cardiac surgery (open surgery on the heart or thoracic aorta) will be automatically enrolled, randomised and allocated to one of two treatment arms in a pragmatic randomised controlled trial at the time of initial extubation. The two treatment arms are administration of heated, humidified, high-flow nasal cannula oxygen postextubation and usual care (treatment at the discretion of the treating provider). The primary outcome will be all-cause reintubation within 48 hours of initial extubation. Secondary outcomes include all-cause 30-day mortality, hospital length of stay, intensive care unit length of stay and ventilator-free days. Interaction analyses will be conducted to assess the differential impact of the intervention within strata of predicted risk of reintubation, calculated according to our previously published and validated prognostic model.

ETHICS AND DISSEMINATION

Vanderbilt University Medical Center IRB approval, 15 March 2021 with waiver of written informed consent. Plan for publication of study protocol prior to study completion, as well as publication of results.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov, NCT04782817 submitted 25 February 2021.

DATE OF PROTOCOL

29 August 2022. Version 2.0.

Prediction and prognosis of reintubation after surgery for Stanford type A aortic dissection

Background

Reintubation is a serious adverse respiratory event after Stanford type A aortic dissection surgery (AADS), however, published studies focused on reintubation after AADS are very limited worldwide. The objectives of the current study were to establish an early risk prediction model for reintubation after AADS and to clarify its relationship with short-term and long-term prognosis.

Methods

Patients undergoing AADS between 2016–2019 in a single institution were identified and divided into two groups based on whether reintubation was performed. Independent predictors were identified by univariable and multivariable analysis and a clinical prediction model was then established. Internal validation was performed using bootstrap method with 1,000 replications. The relationship between reintubation and clinical outcomes was determined by univariable and propensity score matching analysis.

Results

Reintubation were performed in 72 of the 492 included patients (14.6%). Three preoperative and one intraoperative predictors for reintubation were identified by multivariable analysis, including older age, smoking history, renal insufficiency and transfusion of intraoperative red blood cells. The model established using the above four predictors showed moderate discrimination (AUC = 0.753, 95% CI, [0.695–0.811]), good calibration (Hosmer-Lemeshow χ² value = 3.282, P = 0.915) and clinical utility. Risk stratification was performed and three risk intervals were identified. Reintubation was closely associated with poorer in-hospital outcomes, however, no statistically significant association between reintubation and long-term outcomes has been observed in patients who were discharged successfully after surgery.

Conclusions

The requirement of reintubation after AADS is prevalent, closely related to adverse in-hospital outcomes, but there is no statistically significant association between reintubation and long-term outcomes. Predictors were identified and a risk model predicting reintubation was established, which may have clinical utility in early individualized risk assessment and targeted intervention.