Prospective Validation of a Simple Risk Score to Predict Hospitalization during the Omicron Phase of COVID-19.

INTRODUCTION: We previously developed a simple risk score with 3 items (age, patient report of dyspnea, and any relevant comorbidity), and in this report validate it in a prospective sample of patients, stratified by vaccination status. METHODS: Data were abstracted from a structured electronic health record of primary care and urgent care 8 patients with COVID-19 in the Lehigh Valley Health Network from 11/21/2021 and 10/31/2022 9 (Omicron variant). Our previously derived risk score was calculated for each of 19,456 patients, 10 and the likelihood of hospitalization was determined. Area under the ROC curve was calculated. RESULTS: We were able to place 13,239 patients (68%) in a low-risk group with only a 0.16% risk of 13 hospitalization. The moderate risk group with 5622 patients had a 2.2% risk of hospitalization 14 and might benefit from close outpatient follow-up, whereas the high-risk group with only 574 15 patients (2.9% of all patients) had an 8.9% risk of hospitalization and may require further 16 evaluation. Area under the curve was 0.844. DISCUSSION: We prospectively validated a simple risk score for primary and urgent care patients with COVID1919 that can support outpatient triage decisions around COVID-19.

Validation of three health literacy screening questions compared with S-TOFHLA in a low-income diverse English- and Spanish-Speaking population.

BACKGROUND: Clinicians need a tool to gauge patients' ability to understand health conditions and treatment options. The Short-form Test of Functional Health Literacy in Adults (S-TOFHLA) is the gold standard for this, but its length is prohibitive for use in clinical settings. This study seeks to validate a novel three-item question set for predicting health literacy. METHODS: This cross-sectional study utilized an in-person questionnaire alongside the S-TOFHLA. The sample included 2027 English- and Spanish-speaking adults (≥18 years) recruited from primary care practices serving a low-income eastern Pennsylvania community. Most patients (57.7%) identified as Hispanic. Diagnostic accuracy of each question and aggregated scores were assessed against the validated survey by calculating the area under the receiver operating characteristic (AUROC) curve. RESULTS: Questions in the 'Problems Learning' and 'Help Reading' domains (AUROC 0.66 for each) performed better than the 'Confident Forms' question (AUROC 0.64). Summing all three scores resulted in an even higher AUROC curve (0.71). Cronbach's alpha of the combined items was 0.696. CONCLUSIONS: Study results suggest that any of the three questions are viable options for screening health literacy levels of diverse patients in primary care clinical settings. However, they perform better as a summed score than when used individually.

Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

A Social Needs Assessment Tool for an Urban Latino Population.

Unmet social needs contribute significantly to health outcomes, yet they are not routinely assessed in health care settings. Identifying modifiable social needs and feasible tools to assess them may improve health and decrease costs. We conducted 18 focus groups with 115 participants, stratified by age (18-35, 36-64, and 65+), ethnicity (Hispanic, non-Hispanic), and language (English, Spanish) to explore priority social needs, images to depict social need categories, and acceptability of a computer-based program to identify these needs. The top three social need domains were access to care, health promoting behaviors, and family responsibilities. Participants voiced diverse social needs with notable differences across demographic groups. Both the 36-64 year old age groups and Spanish-speaking Hispanic patients were disproportionately impacted by unmet social needs. Perceptions regarding use of an interactive computer program to assess social needs varied by age. Most participants noted that a tablet computer was an acceptable venue to share social needs, though a tutorial may be needed for patients in the 65 and older group. Lastly, participants' ranking of icons were compiled to identify recognizable images of social need categories for those with literacy challenges. Unmet social needs were identified across all groups. This composite of information (priority social needs and images to represent them) will allow for creation of a tailored social need screening tool within an urban Hispanic population.