Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

Objectives

We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19).

Material and methods

This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1–33%), moderate (34–66%), or severe (67–100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis.

Results

Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia.

Conclusions

COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients

BACKGROUND

The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort.

METHODS

With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224).

RESULTS

30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data.

CONCLUSIONS

The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.

The Impact of COVID-19 and Pandemic Mitigation Measures on Persons With Sensory Impairment

PURPOSE

To assess the impact of the COVID-19 pandemic and associated mitigation measures on persons with sensory impairments (SI), including visual impairments (VI) and hearing impairments (HI).

DESIGN

Cross-sectional survey.

METHODS

Adults with VI (best-corrected visual acuity <20/60 in the better-seeing eye), HI (International Classification of Diseases, Tenth Revision, codes), and age- and sex-matched controls (n = 375) were recruited from the University of Michigan. The 34-item Coronavirus Disability Survey was administered. Both χ² tests and logistic regression were used to compare survey responses between groups.

RESULTS

All groups reported high levels of disruption of daily life, with 80% reporting "a fair amount" or "a lot" of disruption (VI: 76%, HI: 83%, CT: 82%, P = .33). Participants with VI had greater difficulty with day-to-day activities and were more likely to cite the following reasons: caregiver was worried about COVID-19 (odds ratio [OR]_VI = 7.2, 95% CI = 3.5-14.4, P < .001) and decreased availability of public transportation (OR_VI = 5.0, 95% CI = 1.5-15.6, P = .006). Participants with VI, but not HI, showed a trend toward increased difficulty accessing medical care (OR_VI = 2.0, 95% CI = 0.99-4.0, P = .052) and began relying more on others for day-to-day assistance (OR_VI = 3.1, 95% CI = 1.6-5.7, P < .001). Overall, 30% reported difficulty obtaining trusted information about the pandemic. Those with VI reported more difficulty seeing or hearing trusted information (OR_VI = 6.1, 95% CI = 1.6-22.1, P = .006). Employed participants with HI were more likely to report a reduction in wages (OR_HI = 2.5, 95% CI = 1.2-5.3, P = .02).

CONCLUSIONS

Individuals with VI have experienced increased disruption and challenges in daily activities related to the pandemic. People with SI may benefit from targeted policy approaches to the current pandemic and future stressors. Minimal differences in some survey measures may be due to the large impact of the pandemic on the population as a whole. The SARS-CoV-2 (COVID-19) pandemic and public health mitigation measures have had an exceedingly large impact around the globe. As of the time of writing, more than 114 million global cases (28 million US) had been diagnosed, and there had been more than 2.5 million fatalities attributed to COVID-19 (517,000 US).^1,2.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

Importance

The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.

Objectives

To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity.

Design, Setting, and Participants

In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation).

Main Outcomes and Measures

Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression.

Results

The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity.

Conclusions and Relevance

This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.