Performance of Cardiovascular Physical Exam Skills by Internal Medicine Residents.

OBJECTIVE: Few studies have assessed the ability of internal medicine residents to perform a cardiovascular physical examination using real patients. METHODS: First year internal medicine interns from two large academic medical centers in Maryland examined the same patient with aortic insufficiency as part of the Assessment of Physical Examination and Communication Skills (APECS). Interns were assessed on five clinical domains: physical exam technique, identifying physical signs, generating a differential diagnosis, clinical judgement, and maintaining patient welfare. Spearman's correlation test was used to describe associations between clinical domains. Preceptor comments were examined to identify common errors in physical exam technique and identifying physical signs. RESULTS: One-hundred and nine interns examined the same patient with aortic insufficiency across 14 APECS sessions. Only 58 interns (53.2%) correctly identified the presence of a diastolic murmur, and only 52 interns (47.7%) included aortic insufficiency on their differential diagnosis. There was a significant and positive correlation between physical exam technique and identification of the correct physical findings (r=0.42, p<0.001). Both technique (r=0.34, p=0.003) and identifying findings (r=0.42, p<0.001) were significantly associated with generating an appropriate differential diagnosis. Common errors in technique included auscultating over the gown, timing the cardiac cycle with the radial pulse, and failing to palpate for the apical impulse. CONCLUSIONS: Internal medicine interns had variable skills in performing and interpreting the cardiovascular physical exam. Improving cardiovascular exam skills would likely lead to increased identification of relevant cardiovascular findings, inform clinical decision making and improve overall patient care.

Lack of racial and ethnic disparities in mortality in minority patients hospitalised with COVID-19 in a mid-Atlantic healthcare system.

INTRODUCTION: In the USA, minoritised communities (racial and ethnic) have suffered disproportionately from COVID-19 compared with non-Hispanic white communities. In a large cohort of patients hospitalised for COVID-19 in a healthcare system spanning five adult hospitals, we analysed outcomes of patients based on race and ethnicity. METHODS: This was a retrospective cohort analysis of patients 18 years or older admitted to five hospitals in the mid-Atlantic area between 4 March 2020 and 27 May 2022 with confirmed COVID-19. Participants were divided into four groups based on their race/ethnicity: non-Hispanic black, non-Hispanic white, Latinx and other. Propensity score weighted generalised linear models were used to assess the association between race/ethnicity and the primary outcome of in-hospital mortality. RESULTS: Of the 9651 participants in the cohort, more than half were aged 18-64 years old (56%) and 51% of the cohort were females. Non-Hispanic white patients had higher mortality (p<0.001) and longer hospital length-of-stay (p<0.001) than Latinx and non-Hispanic black patients. DISCUSSION: In this large multihospital cohort of patients admitted with COVID-19, non-Hispanic black and Hispanic patients did not have worse outcomes than white patients. Such findings likely reflect how the complex range of factors that resulted in a life-threatening and disproportionate impact of incidence on certain vulnerable populations by COVID-19 in the community was offset through admission at well-resourced hospitals and healthcare systems. However, there continues to remain a need for efforts to address the significant pre-existing race and ethnicity inequities highlighted by the COVID-19 pandemic to be better prepared for future public health emergencies.

Does nurse use of a standardized flowsheet to document communication with advanced providers provide a mechanism to detect pulse oximetry failures? A retrospective study of electronic health record data.

BACKGROUND: Pulse oximetry guides clinical decisions, yet does not uniformly identify hypoxemia. We hypothesized that nursing documentation of notifying providers, facilitated by a standardized flowsheet for documenting communication to providers (physicians, nurse practitioners, and physician assistants), may increase when hypoxemia is present, but undetected by the pulse oximeter, in events termed "occult hypoxemia." OBJECTIVE: To compare nurse documentation of provider notification in the 4 h preceding cases of occult hypoxemia, normal oxygenation, and evident hypoxemia confirmed by an arterial blood gas reading. METHODS: We conducted a retrospective study using electronic health record data from patients with COVID-19 at five hospitals in a healthcare system with paired SpO2 and SaO2 readings (measurements within 10 min of oxygen saturation levels in arterial blood, SaO2, and by pulse oximetry, SpO2). We applied multivariate logistic regression to assess if having any nursing documentation of provider notification in the 4 h prior to a paired reading confirming occult hypoxemia was more likely compared to a paired reading confirming normal oxygen status, adjusting for characteristics significantly associated with nursing documentation. We applied conditional logistic regression to assess if having any nursing documentation of provider notification was more likely in the 4-hour window preceding a paired reading compared to the 4-hour window 24 h earlier separately for occult hypoxemia, visible hypoxemia, and normal oxygenation. RESULTS: There were data from 1910 patients hospitalized with COVID-19 who had 44,972 paired readings and an average of 26.5 (34.5) nursing documentation of provider notification events. The mean age was 63.4 (16.2). Almost half (866/1910, 45.3 %) were White, 701 (36.7 %) were Black, and 239 (12.5 %) were Hispanic. Having any nursing documentation of provider notification was 46 % more common in the 4 h before an occult hypoxemia paired reading compared to a normal oxygen status paired reading (OR 1.46, 95 % CI: 1.28-1.67). Comparing the 4 h immediately before the reading to the 4 h one day preceding the paired reading, there was a higher likelihood of having any nursing documentation of provider notification for both evident (OR 1.45, 95 % CI 1.24-1.68) and occult paired readings (OR 1.26, 95 % CI 1.04-1.53). CONCLUSION: This study finds that nursing documentation of provider notification significantly increases prior to confirmed occult hypoxemia, which has potential in proactively identifying occult hypoxemia and other clinical issues. There is potential value to encouraging standardized documentation of nurse concern, including communication to providers, to facilitate its inclusion in clinical decision-making.

Preparing the Frontlines: Delivering Special Pathogen Training to Maryland Hospital Staff.

Healthcare workers (HCWs) at community hospitals, also known as frontline hospitals (FLHs), may encounter patients with possible infectious diseases, including those caused by high-consequence pathogens such as Zaire ebolavirus. We created and piloted a 1-day, in-person, didactic and skills training program to determine the feasibility and acceptability of implementing an educational program to enhance the knowledge and skills needed to respond when a patient with a potential high-consequence pathogen presents to an FLH. The Maryland Department of Health queried all 104 state FLHs to identify their interest in participating in the pilot training program. HCWs from 12 (75%) of the 16 interested FLHs participated in the program before it was interrupted by the COVID-19 pandemic. In addition to pathogen-specific training based on the Identify, Isolate, and Inform framework, we provided skills training in the proper use of personal protective equipment, spill cleanup, and removal of an incapacitated HCW from an isolation area. We conducted a paired pretraining and posttraining knowledge assessment and measured a significant learning gain among 135 participants (2-tailed t test, P<.05). Over 95% of the participants reported that the training was relevant to their daily work and the clinical simulations and reference material were useful and appropriate for their learning level. Findings from this pilot program demonstrated the feasibility and acceptability of a 1-day combined didactic and skills training program focused on high-consequence pathogens. We plan to reengage the original FLHs and add regional FLHs in an updated training effort based on our findings.

The ethics of using COVID-19 host genomic information for clinical and public health decision-making: A survey of US health professionals.

Several genetic variants linked to COVID-19 have been identified by host genomics researchers. Further advances in this research will likely play a role in the clinical management and public health control of future infectious disease outbreaks. The implementation of genetic testing to identify host genomic risk factors associated with infectious diseases raises several ethical, legal, and social implications (ELSIs). As an important stakeholder group, health professionals can provide key insights into these ELSI issues. In 2021, a cross-sectional online survey was fielded to US health professionals. The survey explored how they view the value and ethical acceptability of using COVID-19 host genomic information in three main decision-making settings: (1) clinical, (2) public health, and (3) workforce. The survey also assessed participants' personal and professional experience with genomics and infectious diseases and collected key demographic data. A total of 603 participants completed the survey. A majority (84%) of participants agreed that it is ethically acceptable to use host genomics to make decisions about clinical care and 73% agreed that genetic screening has an important role to play in the public health control of COVID-19. However, more than 90% disagreed that it is ethically acceptable to use host genomics to deny resources or admission to individuals when hospital resources are scarce. Understanding stakeholder perspectives and anticipating ELSI issues will help inform policies for hospitals and public health departments to evaluate and perhaps adopt host genomic technologies in an ethically and socially responsible manner during future infectious disease outbreaks.

Navigating the Landscape of Precision Education: Insights From On-the-Ground Initiatives.

ABSTRACT: A central goal of precision education (PE) is efficiently delivering the right educational intervention to the right learner at the right time. This can be achieved through a PE cycle that involves gathering inputs, using analytics to generate insights, planning and implementing interventions, learning and assessing outcomes, and then using lessons learned to inform modifications to the cycle. In this paper, the authors describe 3 PE initiatives utilizing this cycle. The Graduate Medical Education Laboratory (GEL) uses longitudinal data on graduate trainee behavior, clinical skills, and wellness to improve clinical performance and professional fulfillment. The Transition to Residency Advantage (TRA) program uses learner data from medical school coupled with individualized coaching to improve the transition to residency. The Anesthesia Research Group for Educational Technology (TARGET) is developing an automated tool to deliver individualized education to anesthesia residents based on a longitudinal digital representation of the learner. The authors discuss strengths of the PE cycle and transferrable learnings for future PE innovations. Common challenges are identified, including related to data (e.g., volume, variety, sharing across institutions, using the electronic health record), analytics (e.g., validating augmented intelligence models), and interventions (e.g., scaling up learner assessments with limited resources). PE developers need to share their experiences in order to overcome these challenges, develop best practices, and ensure ethical development of future systems. Adapting a common framework to develop and assess PE initiatives will lead to a clearer understanding of their impact, help to mitigate potential risks, and allow deployment of successful practices on a larger scale.

Skin Pigmentation and Pulse Oximeter Accuracy in the Intensive Care Unit: a Pilot Prospective Study.

Rationale: Despite multiple reports of pulse oximeter inaccuracy among hospitalized Black individuals, regulatory testing of pulse oximeters is performed on healthy volunteers. Objective: Evaluate pulse oximeter accuracy among intensive care unit patients with diverse skin pigmentation. Methods: Skin pigmentation was measured using a chromameter in 12 patients and individual typology angle (ITA), a measure of constitutive pigmentation, calculated. Arterial blood gas (ABG) arterial oxygen saturation (SaO 2 ) sampling was precisely matched to pulse oximetry (SpO 2 ) using arterial line waveforms analysis. Error (SpO 2 -SaO 2 ), bias, and average root mean square error (A RMS ) were calculated. Multivariable linear mixed effects models evaluated the association of SpO 2 -SaO 2 with skin pigmentation. Measurements and Main Results: Sampling time was determined for 350 ABGs. Five participants (N=96 ABGs) were darkly pigmented (forehead ITA<-30°), and 7 lighter pigmented (N=254 ABGs). Darkly pigmented individuals had 1.05% bias and 4.15% A RMS compared to 0.34% bias and 1.97% A RMS among lighter pigmented individuals. After adjusting for SaO 2 , pH, heart rate, and mean arterial pressure, SpO 2 -SaO 2 was falsely elevated by 1.00% more among darkly pigmented individuals (95% confidence interval: 0.25-1.76%). SpO 2 significantly overestimated SaO 2 for dark, brown, and tan forehead or forearm pigmentation and brown and tan finger pad pigmentation compared to intermediate/light pigmentation. Conclusions: The pulse oximeter in clinical use at an academic medical center performed worse in darkly pigmented critically ill patients than established criteria for FDA clearance. Pulse oximeter testing in ICU settings is feasible, and could be required by regulators to ensure equivalent device performance by skin pigmentation among patients.

Information Challenges Associated With Accessing and Sharing of Patient Information in Disasters: A Qualitative Analysis.

As disasters increase in frequency and severity, so too does the health impact on affected populations. Disasters exacerbate the already challenging health information-sharing landscape. A reduced capacity to access and share patient information may have negative impacts on providers' ability to care for patients individually and to address disaster health outcomes at the population level. Between October 2018 and July 2019, we conducted 21 semistructured interviews with physicians experienced in providing healthcare during disasters to understand the challenges related to patient information sharing in disaster responses. Key informants noted challenges with patient information management-including accessing, sharing, and transferring information-and that it was a barrier to providing effective clinical care in disasters. Three major areas were identified as challenges: (1) lack of systematic mechanisms for patient information sharing during disaster handoffs, (2) lack of access to a patient's past medical history, and (3) population-level impacts of patient information-sharing breakdowns in disasters. Reducing barriers to effective patient information sharing is a critical need during disasters. Requirements generally fall to overburdened clinicians, and novel solutions that ease this responsibility and leverage existing infrastructure should be explored. Work conducted during the COVID-19 pandemic may inform new solutions. Integrated approaches that support information sharing in real time will improve patient care at the individual level and can support operational improvements to current and future disaster responses.

Clinical Outcomes Associated With Overestimation of Oxygen Saturation by Pulse Oximetry in Patients Hospitalized With COVID-19.

Importance: Many pulse oximeters have been shown to overestimate oxygen saturation in persons of color, and this phenomenon has potential clinical implications. The relationship between overestimation of oxygen saturation with timing of COVID-19 medication delivery and clinical outcomes remains unknown. Objective: To investigate the association between overestimation of oxygen saturation by pulse oximetry and delay in administration of COVID-19 therapy, hospital length of stay, risk of hospital readmission, and in-hospital mortality. Design, Setting, and Participants: This cohort study included patients hospitalized for COVID-19 at 186 acute care facilities in the US with at least 1 functional arterial oxygen saturation (SaO2) measurement between March 2020 and October 2021. A subset of patients were admitted after July 1, 2020, without immediate need for COVID-19 therapy based on pulse oximeter saturation (SpO2 levels of 94% or higher without supplemental oxygen). Exposures: Self-reported race and ethnicity, difference between concurrent SaO2 and pulse oximeter saturation (SpO2) within 10 minutes, and initially unrecognized need for COVID-19 therapy (first SaO2 reading below 94% despite SpO2 levels of 94% or above). Main Outcome and Measures: The association of race and ethnicity with degree of pulse oximeter measurement error (SpO2 - SaO2) and odds of unrecognized need for COVID-19 therapy were determined using linear mixed-effects models. Associations of initially unrecognized need for treatment with time to receipt of therapy (remdesivir or dexamethasone), in-hospital mortality, 30-day hospital readmission, and length of stay were evaluated using mixed-effects models. All models accounted for demographics, clinical characteristics, and hospital site. Effect modification by race and ethnicity was evaluated using interaction terms. Results: Among 24 504 patients with concurrent SpO2 and SaO2 measurements (mean [SD] age, 63.9 [15.8] years; 10 263 female [41.9%]; 3922 Black [16.0%], 7895 Hispanic [32.2%], 2554 Asian, Native American or Alaskan Native, Hawaiian or Pacific Islander, or another race or ethnicity [10.4%], and 10 133 White [41.4%]), pulse oximetry overestimated SaO2 for Black (adjusted mean difference, 0.93 [95% CI, 0.74-1.12] percentage points), Hispanic (0.49 [95% CI, 0.34-0.63] percentage points), and other (0.53 [95% CI, 0.35-0.72] percentage points) patients compared with White patients. In a subset of 8635 patients with a concurrent SpO2 - SaO2 pair without immediate need for COVID-19 therapy, Black patients were significantly more likely to have pulse oximetry values that masked an indication for COVID-19 therapy compared with White patients (adjusted odds ratio [aOR], 1.65; 95% CI, 1.33-2.03). Patients with an unrecognized need for COVID-19 therapy were 10% less likely to receive COVID-19 therapy (adjusted hazard ratio, 0.90; 95% CI, 0.83-0.97) and higher odds of readmission (aOR, 2.41; 95% CI, 1.39-4.18) regardless of race (P for interaction = .45 and P = .14, respectively). There was no association of unrecognized need for COVID-19 therapy with in-hospital mortality (aOR, 0.84; 95% CI, 0.71-1.01) or length of stay (mean difference, -1.4 days; 95% CI, -3.1 to 0.2 days). Conclusions and Relevance: In this cohort study, overestimation of oxygen saturation by pulse oximetry led to delayed delivery of COVID-19 therapy and higher probability of readmission regardless of race. Black patients were more likely to have unrecognized need for therapy with potential implications for population-level health disparities.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.

BACKGROUND: There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. OBJECTIVE: The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. METHODS: This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. RESULTS: A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. CONCLUSIONS: Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Potential drug-drug interactions among U.S. adults treated with nirmatrelvir/ritonavir: A cross-sectional study of the National Covid Cohort Collaborative (N3C).

STUDY OBJECTIVE: To estimate the prevalence of potential moderate to severe drug-drug interactions (DDIs) involving nirmatrelvir/ritonavir, identify interacting medications, and evaluate risk factors associated with potential DDIs. DESIGN: Cross-sectional study. DATA SOURCE: Electronic health records from the National COVID Cohort Collaborative Enclave, one of the largest COVID-19 data resources in the United States. PATIENTS: Outpatients aged ≥18 years and started nirmatrelvir/ritonavir between December 23, 2021 and March 31, 2022. INTERVENTION: Nirmatrelvir/ritonavir. MEASUREMENTS: The outcome is potential moderate to severe DDIs, defined as starting interacting medications reported by National Institutes of Health 30 days before or 10 days after starting nirmatrelvir/ritonavir. MAIN RESULTS: Of 3214 outpatients who started nirmatrelvir/ritonavir, the mean age was 56.8 ± 17.1 years, 39.5% were male, and 65.8% were non-Hispanic white. Overall, 521 (16.2%) were potentially exposed to at least one moderate to severe DDI, most commonly to atorvastatin (19.2% of all DDIs), hydrocodone (14.0%), or oxycodone (14.0%). After adjustment for covariates, potential DDIs were more likely among individuals who were older (odds ratio [OR] 1.16 per 10-year increase, 95% confidence interval [CI] 1.08-1.25), male (OR 1.36, CI 1.09-1.71), smokers (OR 1.38, CI 1.10-1.73), on more co-medications (OR 1.35, CI 1.31-1.39), and with a history of solid organ transplant (OR 3.63, CI 2.05-6.45). CONCLUSIONS: One in six of individuals receiving nirmatrelvir/ritonavir were at risk of a potential moderate or severe DDI, underscoring the importance of clinical and pharmacy systems to mitigate such risks.

Internal medicine intern performance on the gastrointestinal physical exam.

OBJECTIVES: The gastrointestinal (GI) physical exam provides critical information about underlying disease states. However, since assessment of physical examination skills is rarely conducted as part of internal medicine residency training, little is known about resident performance on the GI physical exam. METHODS: During a clinical skills assessment that took place between November 2019 and February 2020, internal medicine interns examined the same patient with chronic liver disease while being observed by faculty preceptors. We compared the exam maneuvers performed with those expected by the faculty evaluators. We noted which maneuvers were performed incorrectly, whether physical exam technique correlated with identification of physical exam findings, and if performance on the physical exam was associated with building an appropriate differential diagnosis. This four-hour assessment was required for internal medicine interns within two different residency programs in the Baltimore area. RESULTS: More than half of the 29 participating interns (n=17, 58.6 %) received a "needs improvement" score on their physical exam technique. Technique was highly correlated with identifying the correct physical signs (r=0.88, p<0.0001). The most commonly excluded maneuvers were assessing for splenomegaly and hepatomegaly. The most commonly missed findings were splenomegaly and hepatomegaly. Most interns included chronic liver disease as part of their differential diagnosis even if they received "needs improvement" scores on physical exam technique or identifying physical signs. CONCLUSIONS: Internal medicine interns would benefit from learning an organized approach to the gastrointestinal exam. This would likely lead to increased identification of important gastrointestinal findings.

Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19.

RATIONALE & OBJECTIVE: Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers. EXPOSURE: Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization. OUTCOME: MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days. ANALYTICAL APPROACH: Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index. RESULTS: The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively. LIMITATIONS: No control group of hospitalized patients without COVID-19. CONCLUSIONS: We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes. PLAIN-LANGUAGE SUMMARY: Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes.

TIMS: A Mixed Methods Evaluation of the Impact of a Novel Chaplain Facilitated Recorded Interview Placed in the Medical Chart for the Medical Staff in an ICU During the COVID-19 Pandemic.

The purpose of this study is to examine how a novel intervention known as TIMS, "This is My Story," impacted clinicians caring for patients during the COVID-19 pandemic in the medical intensive care unit (MICU) at the Johns Hopkins Hospital. An eight-question survey was administered to MICU staff on their experience with TIMS files for pre- and post-listening reflections. Qualitative interviews were conducted with 17 staff members who prospectively agreed to participate. A total of 97 pre-listening and 88 post-listening questionnaires were completed. Responses indicated that the audio recording was appropriate to discover more about the patient beyond the immediately observable and useful (98%), "considerably" increased staff empathy for the patient (74%), and thought it would "some" or "considerably" improve subsequent interactions with the patient's loved ones (99%). The qualitative analysis revealed that medical staff found the audio format easy to use and helpful in humanizing patients in their clinical practice. The study demonstrates that TIMS audio files are an important addition to the electronic medical record, enabling clinicians to practice with greater awareness of the patient's context and increased empathy for patients and families.

COVID-19 clinical outcomes by patient disability status: A retrospective cohort study.

BACKGROUND: People with disabilities might experience worse clinical outcomes of SARS-CoV-2 infection, but evidence is limited. OBJECTIVE: To investigate if people with disabilities requiring assistance are more likely to experience severe COVID-19 or death. METHODS: Data from the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN) included 6494 adult patients diagnosed with COVID-19 and admitted between March 4, 2020-October 29, 2021. Severe COVID-19 and death were defined using the occurrence and timing of clinical events. Assistive needs due to disabilities were reported by patients or their proxies upon admission. Multivariable-adjusted Cox proportional hazards models were used to examine the associations between disability status and severe COVID-19 or death. Primary models adjusted for demographics and secondary models additionally adjusted for clinical covariates. RESULTS: In this clinical cohort (47-73 years, 49% female, 39% Black), patients with disabilities requiring assistance had 1.35 times (95% confidence interval [CI]:1.01, 1.81) the hazard of severe COVID-19 among patients <65 years, but not among those ≥65 years, equating to an additional 17.5 severe COVID-19 cases (95% CI:7.7, 28.2) per 100 patients. A lower risk of mortality was found among patients <65 years, but this finding was not robust due to the small number of deaths. CONCLUSIONS: People with disabilities requiring assistance aged <65 years are more likely to develop severe COVID-19. Although our study is limited by using a medical model of disability, these analyses intend to further our understanding of COVID-19 outcomes among people with disabilities. Also, standardized disability data collection within electronic health records is needed.

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Variants on Inpatient Clinical Outcome.

BACKGROUND: Prior observation has shown differences in COVID-19 hospitalization risk between SARS-CoV-2 variants, but limited information describes hospitalization outcomes. METHODS: Inpatients with COVID-19 at 5 hospitals in the eastern United States were included if they had hypoxia, tachypnea, tachycardia, or fever, and SARS-CoV-2 variant data, determined from whole-genome sequencing or local surveillance inference. Analyses were stratified by history of SARS-CoV-2 vaccination or infection. The average effect of SARS-CoV-2 variant on 28-day risk of severe disease, defined by advanced respiratory support needs, or death was evaluated using models weighted on propensity scores derived from baseline clinical features. RESULTS: Severe disease or death within 28 days occurred for 977 (29%) of 3369 unvaccinated patients and 269 (22%) of 1230 patients with history of vaccination or prior SARS-CoV-2 infection. Among unvaccinated patients, the relative risk of severe disease or death for Delta variant compared with ancestral lineages was 1.30 (95% confidence interval [CI]: 1.11-1.49). Compared with Delta, the risk for Omicron patients was .72 (95% CI: .59-.88) and compared with ancestral lineages was .94 (.78-1.1). Among Omicron and Delta infections, patients with history of vaccination or prior SARS-CoV-2 infection had half the risk of severe disease or death (adjusted hazard ratio: .40; 95% CI: .30-.54), but no significant outcome difference by variant. CONCLUSIONS: Although risk of severe disease or death for unvaccinated inpatients with Omicron was lower than with Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated inpatients, with no difference between Delta and Omicron infections.

Thromboprophylaxis in people hospitalized with COVID-19: Assessing intermediate or standard doses in a retrospective cohort study.

Background and Objectives: Current clinical guidelines recommend thromboprophylaxis for adults hospitalized with coronavirus disease 2019 (COVID-19), yet it is unknown whether higher doses of thromboprophylaxis offer benefits beyond standard doses. Methods: We studied electronic health records from 50 091 adults hospitalized with COVID-19 in the United States between February 2020 and February 2021. We compared standard (enoxaparin 30 or 40 mg/day, fondaparinux 2.5 mg, or heparin 5000 units twice or thrice per day) versus intermediate (enoxaparin 30 or 40 mg twice daily, or up to 1.2 mg/kg of body weight daily, heparin 7500 units thrice per day or heparin 10 000 units twice or thrice per day) thromboprophylaxis. We separately examined risk of escalation to therapeutic anticoagulation, severe disease (first occurrence of high-flow nasal cannula, noninvasive positive pressure ventilation or invasive mechanical ventilation), and death. To summarize risk, we present hazard ratios (HRs) with 95% confidence intervals (CIs) using adjusted time-dependent Cox proportional hazards regression models. Results: People whose first dose was high intensity were younger, more often obese, and had greater oxygen support requirements. Intermediate dose thromboprophylaxis was associated with increased risk of therapeutic anticoagulation (HR, 3.39; 95% CI, 3.22-3.57), severe disease (HR, 1.22; 95% CI, 1.17-1.28), and death (HR, 1.37; 95% CI, 1.21-1.55). Increased risks associated with intermediate-dose thromboprophylaxis persisted in subgroup and sensitivity analyses varying populations and definitions of exposures, outcomes, and covariates. Conclusions: Our findings do not support routine use of intermediate-dose thromboprophylaxis to prevent clinical worsening, severe disease, or death among adults hospitalized with COVID-19.