Medically ready for discharge: A multisite "point-in-time" assessment of hospitalized patients.

BACKGROUND: Time spent awaiting discharge after the acute need for hospitalization has resolved is an important potential contributor to hospital length of stay (LOS). OBJECTIVE: To measure the prevalence, impact, and context of patients who remain hospitalized for prolonged periods after completion of acute care needs. DESIGN, SETTING, AND PARTICIPANTS: We conducted a cross-sectional "point-in-time" survey at each of 15 academic US hospitals using a structured data collection tool with on-service acute care medicine attending physicians in fall 2022. MAIN OUTCOMES AND MEASURES: Primary outcomes were number and percentage of patients considered "medically ready for discharge" with emphasis on those who had experienced a "major barrier to discharge" (medically ready for discharge for ≥1 week). Estimated LOS attributable to major discharge barriers, contributory discharge needs, and associated hospital characteristics were measured. RESULTS: Of 1928 patients sampled, 35.0% (n = 674) were medically ready for discharge including 9.8% (n = 189) with major discharge barriers. Many patients with major discharge barriers (44.4%; 84/189) had spent a month or longer medically ready for discharge and commonly (84.1%; 159/189) required some form of skilled therapy or daily living support services for discharge. Higher proportions of patients experiencing major discharge barriers were found in public versus private, nonprofit hospitals (12.0% vs. 7.2%; p = .001) and county versus noncounty hospitals (14.5% vs. 8.8%; p = .002). CONCLUSIONS: Patients experience major discharge barriers in many US hospitals and spend prolonged time awaiting discharge, often for support needs that may be outside of clinician control.

External validation of the COVID-19 4C mortality score in an urban United States cohort.

BACKGROUND: Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population. METHODS: This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge. RESULTS: This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89). CONCLUSIONS: Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

Association of race/ethnicity and socioeconomic status with COVID-19 30-day mortality at a Philadelphia medical center using a retrospective cohort study.

COVID-19 has disproportionately affected low-income communities and people of color. Previous studies demonstrated that race/ethnicity and socioeconomic status (SES) are not independently correlated with COVID-19 mortality. The purpose of our study is to determine the effect of race/ethnicity and SES on COVID-19 30-day mortality in a diverse, Philadelphian population. This is a retrospective cohort study in a single-center tertiary care hospital in Philadelphia, PA. The study includes adult patients hospitalized with polymerase-chain-reaction-confirmed COVID-19 between March 1, 2020 and June 6, 2020. The primary outcome was a composite of COVID-19 death or hospice discharge within 30 days of discharge. The secondary outcome was intensive care unit (ICU) admission. The study included 426 patients: 16.7% died, 3.3% were discharged to hospice, and 20.0% were admitted to the ICU. Using multivariable analysis, race/ethnicity was not associated with the primary nor secondary outcome. In Model 4, age greater than 75 (odds ratio [OR]: 11.01; 95% confidence interval [CI]: 1.96-61.97) and renal disease (OR: 2.78; 95% CI: 1.31-5.90) were associated with higher odds of the composite primary outcome. Living in a "very-low-income area" (OR: 0.29; 95% CI: 0.12-0.71) and body mass index (BMI) 30-35 (OR: 0.24; 95% CI: 0.08-0.69) were associated with lower odds of the primary outcome. When controlling for demographics, SES, and comorbidities, race/ethnicity was not independently associated with the composite primary outcome. Very-low SES, as extrapolated from census-tract-level income data, was associated with lower odds of the composite primary outcome.

COVID-19 treatment combinations and associations with mortality in a large multi-site healthcare system.

INTRODUCTION: During the early months of the COVID-19 pandemic, mortality associated with the disease declined in the United States. The standard of care for pharmacological interventions evolved during this period as new and repurposed treatments were used alone and in combination. Though these medications have been studied individually, data are limited regarding the relative impact of different medication combinations. The objectives of this study were to evaluate the association of COVID-19-related mortality and observed medication combinations and to determine whether changes in medication-related practice patterns and measured patient characteristics, alone, explain the decline in mortality seen early in the COVID-19 pandemic. METHODS: A retrospective cohort study was conducted at a multi-hospital healthcare system exploring the association of mortality and combinations of remdesivir, corticosteroids, anticoagulants, tocilizumab, and hydroxychloroquine. Multivariable logistic regression was used to identify predictors of mortality for both the overall population and the population stratified by intensive care and non-intensive care unit admissions. A separate model was created to control for the change in unmeasured variables over time. RESULTS: For all patients, four treatment combinations were associated with lower mortality: Anticoagulation Only (OR 0.24, p < 0.0001), Anticoagulation and Remdesivir (OR 0.25, p = 0.0031), Anticoagulation and Corticosteroids (OR 0.53, p = 0.0263), and Anticoagulation, Corticosteroids and Remdesivir (OR 0.42, p = 0.026). For non-intensive care unit patients, the same combinations were significantly associated with lower mortality. For patients admitted to the intensive care unit, Anticoagulation Only was the sole treatment category associated with decreased mortality. When adjusted for demographics, clinical characteristics, and all treatment combinations there was an absolute decrease in the mortality rate by 2.5% between early and late periods of the study. However, when including an additional control for changes in unmeasured variables overtime, the absolute mortality rate decreased by 5.4%. CONCLUSIONS: This study found that anticoagulation was the most significant treatment for the reduction of COVID-related mortality. Anticoagulation Only was the sole treatment category associated with a significant decrease in mortality for both intensive care and non-intensive care patients. Treatment combinations that additionally included corticosteroids and/or remdesivir were also associated with decreased mortality, though only in the non-intensive care stratum. Further, we found that factors other than measured changes in demographics, clinical characteristics or pharmacological interventions accounted for an additional decrease in the COVID-19-related mortality rate over time.

Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

BACKGROUND: Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. RESEARCH QUESTION: Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? METHODS: This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). RESULTS: The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. CONCLUSIONS: This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.

Initial Experience in Monitoring QT Intervals Using a Six-lead Contactless Mobile Electrocardiogram in an Inpatient Setting.

Mobile electrocardiograms (ECGs) (mECGs) using smartphone applications are an emerging technology. In the coronavirus disease 2019 (COVID-19) era, minimizing patient contact has gained increasing importance. Additionally, increased QT/corrected QT (QTc) monitoring has concurrently been required. The KardiaMobile 6L ECG device, cleared by the United States Food and Drug Administration (FDA) for recording ECGs, along with the KardiaStation tablet application is a platform (AliveCor, Mountain View, CA, USA) that addresses these two issues. A team of residents, fellows, hospitalists, and cardiologists identified inpatients in need of QT/QTc interval monitoring to pilot the adoption of a system composed of a KardiaMobile 6L ECG device with the accompanying KardiaStation tablet application. Concurrent standard ECGs provided validation. Adoption and performance issues were recorded. Four patients agreed to participate in QT/QTc interval monitoring, three of whom were positive for severe acute respiratory syndrome coronavirus 2 viral infection. After basic instructions were given to the patients and their clinical nurses, all patients recorded mECGs successfully. Patients were able to record their own mECG tracings at least once without any assistance. The 12-lead ECGs and mECGs each showed the correct rhythm, and the measured QTc intervals on each modality were consistently acceptable (< 500 ms). Contactless ECGs were successfully uploaded to KardiaStation for QT/QTc interval measurement and archiving. In this study, we showed that an FDA-cleared product, KardiaMobile 6L, has the ability to provide high-quality contactless ECGs for reliable QT/QTc interval measurements. Hospitalized patients were able to perform recordings when requested after receiving simple instructions at the time of first use. This technology has applications during the COVID-19 pandemic and beyond.

PANCYTOPENIA AND LYMPHOID ORGAN HYPERPLASIA IN A PATIENT WITH GRAVES DISEASE: RESPONSE TO ANTITHYROID DRUG THERAPY.

OBJECTIVE: In rare instances, cytopenias manifest as a complication of thyrotoxicosis. Here, we report a case of Graves disease (GD) thyrotoxicosis presenting as pancytopenia that resolved with antithyroid therapy. METHODS: A 35-year-old male presented with fever and chills following an outpatient colonoscopy. Initial blood work revealed pancytopenia. Workup included viral antigen titers, blood cultures, rheumatologic antibodies, inflammatory markers, immunocompetency, nutrient deficiency, metal toxicity, and malignancy. Bone marrow aspirate was analyzed by microscope, flow cytometry, fluorescence in situ hybridization, and genetic analysis. Computed tomography scan of the chest, abdomen, and pelvis was obtained. Thyroid labs included thyroid-stimulating hormone, total triiodothyronine, free thyroxine, thyroid-stimulating immunoglobulin, anti-thyroid peroxidase antibody, and radioiodine uptake scan. RESULTS: All workup above was non-revelatory except as follows. Imaging revealed thymic hyperplasia and splenomegaly. Thyroid labs revealed thyroid-stimulating hormone <0.02 µIU/mL (reference range is 0.30 to 5.00 µIU/mL), free thyroxine of 4.7 ng/dL (reference range is 0.7 to 1.7 ng/dL), total triiodothyronine of 191 pg/mL (reference range is 90 to 180 pg/mL), thyroid-stimulating immunoglobulin of 522% (reference range is <140%). Bone marrow biopsy was consistent with a reactive process suggesting an infectious or autoimmune process. Radioiodine uptake scan confirmed GD. He was discharged on antithyroid medication. Two-month follow-up labs revealed improved cell counts; his absolute neutrophil count was 1.94 × 109 cells/L (reference range is 1.50 to 8.00 × 109 cells/L), hemoglobin was 12.9 g/dL (reference range is 14.0 to 17.0 g/dL), and platelets were 153 × 109 cells/L (reference range is 140 to 400 × 109 cells/L). Definitive treatment was obtained with 12 mCi of 131-iodine. CONCLUSION: Pancytopenia and lymphoid organ hyperplasia (splenomegaly, thymic hyperplasia, and lymphadenopathy) have been previously reported to be associated with thyrotoxicosis secondary to GD, rarely simultaneously, and manifest from both thyrotoxic and immunologic mechanisms. After excluding alternative life-threatening pathologies, in such presentations, GD should be considered and treated if confirmed.

Circadian misalignment affects sleep and medication use before and during spaceflight.

Sleep deficiency and the use of sleep-promoting medication are prevalent during spaceflight. Operations frequently dictate work during the biological night and sleep during the biological day, which contribute to circadian misalignment. We investigated whether circadian misalignment was associated with adverse sleep outcomes before (preflight) and during spaceflight missions aboard the International Space Station (ISS). Actigraphy and photometry data for 21 astronauts were collected over 3,248 days of long-duration spaceflight on the ISS and 11 days prior to launch (n=231 days). Sleep logs, collected one out of every 3 weeks in flight and daily on Earth, were used to determine medication use and subjective ratings of sleep quality. Actigraphy and photometry data were processed using Circadian Performance Simulation Software to calculate the estimated endogenous circadian temperature minimum. Sleep episodes were classified as aligned or misaligned relative to the estimated endogenous circadian temperature minimum. Mixed-effects regression models accounting for repeated measures were computed by data collection interval (preflight, flight) and circadian alignment status. The estimated endogenous circadian temperature minimum occurred outside sleep episodes on 13% of sleep episodes during preflight and on 19% of sleep episodes during spaceflight. The mean sleep duration in low-Earth orbit on the ISS was 6.4±1.2 h during aligned and 5.4±1.4 h (P<0.01) during misaligned sleep episodes. During aligned sleep episodes, astronauts rated their sleep quality as significantly better than during misaligned sleep episodes (66.8±17.7 vs. 60.2±21.0, P<0.01). Sleep-promoting medication use was significantly higher during misaligned (24%) compared with aligned (11%) sleep episodes (P<0.01). Use of any medication was significantly higher on days when sleep episodes were misaligned (63%) compared with when sleep episodes were aligned (49%; P<0.01). Circadian misalignment is associated with sleep deficiency and increased medication use during spaceflight. These findings suggest that there is an immediate need to deploy and assess effective countermeasures to minimize circadian misalignment and consequent adverse sleep outcomes both before and during spaceflight.

Short-wavelength enrichment of polychromatic light enhances human melatonin suppression potency.

The basic goal of this research is to determine the best combination of light wavelengths for use as a lighting countermeasure for circadian and sleep disruption during space exploration, as well as for individuals living on Earth. Action spectra employing monochromatic light and selected monochromatic wavelength comparisons have shown that short-wavelength visible light in the blue-appearing portion of the spectrum is most potent for neuroendocrine, circadian, and neurobehavioral regulation. The studies presented here tested the hypothesis that broad spectrum, polychromatic fluorescent light enriched in the short-wavelength portion of the visible spectrum is more potent for pineal melatonin suppression in healthy men and women. A total of 24 subjects were tested across three separate experiments. Each experiment used a within-subjects study design that tested eight volunteers to establish the full-range fluence-response relationship between corneal light irradiance and nocturnal plasma melatonin suppression. Each experiment tested one of the three types of fluorescent lamps that differed in their relative emission of light in the short-wavelength end of the visible spectrum between 400 and 500 nm. A hazard analysis, based on national and international eye safety criteria, determined that all light exposures used in this study were safe. Each fluence-response curve demonstrated that increasing corneal irradiances of light evoked progressively increasing suppression of nocturnal melatonin. Comparison of these fluence-response curves supports the hypothesis that polychromatic fluorescent light is more potent for melatonin regulation when enriched in the short-wavelength spectrum.

Prevalence of sleep deficiency and use of hypnotic drugs in astronauts before, during, and after spaceflight: an observational study.

BACKGROUND: Sleep deprivation and fatigue are common subjective complaints among astronauts. Previous studies of sleep and hypnotic drug use in space have been limited to post-flight subjective survey data or in-flight objective data collection from a small number of crew members. We aimed to characterise representative sleep patterns of astronauts on both short-duration and long-duration spaceflight missions. METHODS: For this observational study, we recruited crew members assigned to Space Transportation System shuttle flights with in-flight experiments between July 12, 2001, and July 21, 2011, or assigned to International Space Station (ISS) expeditions between Sept 18, 2006, and March 16, 2011. We assessed sleep-wake timing objectively via wrist actigraphy, and subjective sleep characteristics and hypnotic drug use via daily logs, in-flight and during Earth-based data-collection intervals: for 2 weeks scheduled about 3 months before launch, 11 days before launch until launch day, and for 7 days upon return to Earth. FINDINGS: We collected data from 64 astronauts on 80 space shuttle missions (26 flights, 1063 in-flight days) and 21 astronauts on 13 ISS missions (3248 in-flight days), with ground-based data from all astronauts (4014 days). Crew members attempted and obtained significantly less sleep per night as estimated by actigraphy during space shuttle missions (7·35 h [SD 0·47] attempted, 5·96 h [0·56] obtained), in the 11 days before spaceflight (7·35 h [0·51], 6·04 h [0·72]), and about 3 months before spaceflight (7·40 h [0·59], 6·29 h [0·67]) compared with the first week post-mission (8·01 h [0·78], 6·74 h [0·91]; p<0·0001 for both measures). Crew members on ISS missions obtained significantly less sleep during spaceflight (6·09 h [0·67]), in the 11 days before spaceflight (5·86 h [0·94]), and during the 2-week interval scheduled about 3 months before spaceflight (6·41 h [SD 0·65]) compared with in the first week post-mission (6·95 h [1·04]; p<0·0001). 61 (78%) of 78 shuttle-mission crew members reported taking a dose of sleep-promoting drug on 500 (52%) of 963 nights; 12 (75%) of 16 ISS crew members reported using sleep-promoting drugs. INTERPRETATION: Sleep deficiency in astronauts was prevalent not only during space shuttle and ISS missions, but also throughout a 3 month preflight training interval. Despite chronic sleep curtailment, use of sleep-promoting drugs was pervasive during spaceflight. Because chronic sleep loss leads to performance decrements, our findings emphasise the need for development of effective countermeasures to promote sleep. FUNDING: The National Aeronautics and Space Administration.