Empirical Assessment of COVID-19 Crisis Standards of Care Guidelines

Background

Several states have released Crisis Standards of Care (CSC) guidelines for the allocation of scarce critical care resources. Most guidelines rely on Sequential Organ Failure Assessment (SOFA) scores to maximize lives saved, but states have adopted different stances on whether to maximize long-term outcomes (life-years saved) by accounting for patient comorbidities.

Methods

We compared 4 representative state guidelines with varying approaches to comorbidities and analyzed how CSC prioritization correlates with clinical outcomes. We included 27 laboratory-confirmed COVID-19 patients admitted to ICUs at Brigham and Women's Hospital from March 12 to April 3, 2020. We compared prioritization algorithms from New York, which assigns priority based on SOFA alone; Maryland, which uses SOFA plus severe comorbidities; Pennsylvania, which uses SOFA plus major and severe comorbidities; and Colorado, which uses SOFA plus a modified Charlson comorbidity index.

Results

In pairwise comparisons across all possible pairs, we found that state guidelines frequently resulted in tie-breakers based on age or lottery: New York 100% of the time (100% resolved by lottery), Pennsylvania 86% of the time (18% by lottery), Maryland 93% of the time (35% by lottery), and Colorado: 32% of the time (10% by lottery). The prioritization algorithm with the strongest correlation with 14-day outcomes was Colorado (r_s = -0.483. p = 0.011) followed by Maryland (r_s = -0.394, p =0.042), Pennsylvania (r_s = -0.382, p = 0.049), and New York (r_s = 0). An alternative model using raw SOFA scores alone was moderately correlated with outcomes (r_s = -0.448, p = 0.019).

Conclusions

State guidelines for scarce resource allocation frequently resulted in identical priority scores, requiring tie-breakers based on age or lottery. These findings suggest that state CSC guidelines should be further assessed empirically to understand whether they meet their goals.

Identification of Racial Inequities in Access to Specialized Inpatient Heart Failure Care at an Academic Medical Center

BACKGROUND

Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality.

RESULTS

Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84-0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72-0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race.

CONCLUSIONS

Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Particle-Tracking Microrheology Using Micro-Optical Coherence Tomography

Clinical manifestations of cystic fibrosis (CF) result from an increase in the viscosity of the mucus secreted by epithelial cells that line the airways. Particle-tracking microrheology (PTM) is a widely accepted means of determining the viscoelastic properties of CF mucus, providing an improved understanding of this disease as well as an avenue to assess the efficacies of pharmacologic therapies aimed at decreasing mucus viscosity. Among its advantages, PTM allows the measurement of small volumes, which was recently utilized for an in situ study of CF mucus formed by airway cell cultures. Typically, particle tracks are obtained from fluorescence microscopy video images, although this limits one's ability to distinguish particles by depth in a heterogeneous environment. Here, by performing PTM with high-resolution micro-optical coherence tomography (μOCT), we were able to characterize the viscoelastic properties of mucus, which enables simultaneous measurement of rheology with mucociliary transport parameters that we previously determined using μOCT. We obtained an accurate characterization of dextran solutions and observed a statistically significant difference in the viscosities of mucus secreted by normal and CF human airway cell cultures. We further characterized the effects of noise and imaging parameters on the sensitivity of μOCT-PTM by performing theoretical and numerical analyses, which show that our system can accurately quantify viscosities over the range that is characteristic of CF mucus. As a sensitive rheometry technique that requires very small fluid quantities, μOCT-PTM could also be generally applied to interrogate the viscosity of biological media such as blood or the vitreous humor of the eye in situ.

Initial Clinical Findings of a Mathematical Model to Predict Survival of Head and Neck Cancer

Objectives

(1) Identify clinical features that impact survival for head and neck cancer. (2) Determine the individual contribution to mortality of significant clinical features. (3) Develop a web-based calculator to integrate clinical features and predict survival outcome for individual patients.

Study Design

Analysis of a national cancer database. We fit patient data to the binary-biological model of cancer lethality, a mathematical model designed to predict cancer outcome. The model predicts the risk of cancer death, using information on tumor size, nodal status, and other prognostic factors.

Subjects and Methods

Analysis was carried out on a cohort of ~50,000 patients with head and neck cancer from the Survey, Epidemiology and End-Results (SEER) 2009 data set and validated with a cohort of ~1300 patients from an institutional Massachusetts General Hospital/Massachusetts Eye and Ear Infirmary database. We developed a web-based calculator written in JavaScript, PHP, and HTML.

Results

The risk of death due to head and neck cancer increases monotonically with tumor size. Each positive lymph node is associated with ~14% extra risk of death. Anatomical site, age, race, tumor extension, N stage, and extracapsular spread contribute to mortality. The lethal impact of these prognostics factors can be accurately estimated by the Size + Nodes + PrognosticMarkers (SNAP) method.

Conclusions

This predictive cancer model and web-based calculator provide a basis for estimating the risk of death for head and neck cancer patients by assigning values to the lethal contributions of tumor size, number of positive nodes, anatomical site, tumor extension, N stage, extracapsular spread, age at diagnosis, and race.

Survival Based on Smoking Status in Head and Neck Cancer

Objective: 1) Assess the impact of smoking status on disease-specific and overall survival in head and neck cancer. 2) Identify subsite specific differences in survival related to smoking status.

Method: Retrospective analysis of a single institution database from 1980 through 2008. Patients were categorized by site of primary tumor and smoking status. Kaplan Meier analysis was used to calculate survival, and log rank and chi square analysis were used to identify significant differences.

Results: Significant overall and disease-specific survival differences were observed between current, previous, and non-smokers. Current smokers had a lower survival in all subsites except for oral cavity. The biggest differences were observed in the oropharynx subsite. 5-year disease-specific survival for oropharyngeal cancer was: current smokers 57.0%, non-smokers 86.9%, and previous smokers 81.8% ( P < .05). Similar overall survival differences were observed. Smoking status did not impact on overall or disease-specific survival in oral cavity cancer.

Conclusion: While a significant focus in head and neck cancer has been on HPV and tumor biomarkers, it is important to recognize smoking as an important factor in survival outcome for head and neck cancer. Both nonsmokers and previous smokers have a survival advantage over current smokers.