Heterogeneity of Benefit from Earlier Time-to-Antibiotics for Sepsis.

Rationale: Shorter time-to-antibiotics improves survival from sepsis, particularly among patients in shock. There may be other subgroups for whom faster antibiotics are particularly beneficial.Objectives: Identify patient characteristics associated with greater benefit from shorter time-to-antibiotics.Methods: Observational cohort study of patients hospitalized with community-onset sepsis at 173 hospitals and treated with antimicrobials within 12 hours. We used three approaches to evaluate heterogeneity of benefit from shorter time-to-antibiotics: 1) conditional average treatment effects of shorter (⩽3 h) versus longer (>3-12 h) time-to-antibiotics on 30-day mortality using multivariable Poisson regression; 2) causal forest to identify characteristics associated with greatest benefit from shorter time-to-antibiotics; and 3) logistic regression with time-to-antibiotics modeled as a spline.Measurements and Main Results: Among 273,255 patients with community-onset sepsis, 131,094 (48.0%) received antibiotics within 3 hours. In Poisson models, shorter time-to-antibiotics was associated with greater absolute mortality reduction among patients with metastatic cancer (5.0% [95% confidence interval; CI: 4.3-5.7] vs. 0.4% [95% CI: 0.2-0.6] for patients without cancer, P < 0.001); patients with shock (7.0% [95% CI: 5.8-8.2%] vs. 2.8% [95% CI: 2.7-3.5%] for patients without shock, P = 0.005); and patients with more acute organ dysfunctions (4.8% [95% CI: 3.9-5.6%] for three or more dysfunctions vs. 0.5% [95% CI: 0.3-0.8] for one dysfunction, P < 0.001). In causal forest, metastatic cancer and shock were associated with greatest benefit from shorter time-to-antibiotics. Spline analysis confirmed differential nonlinear associations of time-to-antibiotics with mortality in patients with metastatic cancer and shock.Conclusions: In patients with community-onset sepsis, the mortality benefit of shorter time-to-antibiotics varied by patient characteristics. These findings suggest that shorter time-to-antibiotics for sepsis is particularly important among patients with cancer and/or shock.

Risk of severe COVID-19 infection among adults with prior exposure to children.

Susceptibility and severity of COVID-19 infection vary widely. Prior exposure to endemic coronaviruses, common in young children, may protect against SARS-CoV-2. We evaluated risk of severe COVID-19 among adults with and without exposure to young children in a large, integrated healthcare system. Adults with children 0-5 years were matched 1:1 to adults with children 6-11 years, 12-18 years, and those without children based upon a COVID-19 propensity score and risk factors for severe COVID-19. COVID-19 infections, hospitalizations, and need for intensive care unit (ICU) were assessed in 3,126,427 adults, of whom 24% (N = 743,814) had children 18 years or younger, and 8.8% (N = 274,316) had a youngest child 0-5 years. After 1:1 matching, propensity for COVID-19 infection and risk factors for severe COVID-19 were well balanced between groups. Rates of COVID-19 infection were slightly higher for adults with exposure to older children (incident risk ratio, 1.09, 95% confidence interval, [1.05-1.12] and IRR 1.09 [1.05-1.13] for adults with children 6-11 and 12-18, respectively), compared to those with children 0-5 years, although no difference in rates of COVID-19 illness requiring hospitalization or ICU admission was observed. However, adults without exposure to children had lower rates of COVID-19 infection (IRR 0.85, [0.83-0.87]) but significantly higher rates of COVID-19 hospitalization (IRR 1.49, [1.29-1.73]) and hospitalization requiring ICU admission (IRR 1.76, [1.19-2.58]) compared to those with children aged 0-5. In a large, real-world population, exposure to young children was associated with less severe COVID-19 illness. Endemic coronavirus cross-immunity may play a role in protection against severe COVID-19.

Clinical outcomes in hospitalized plasma and platelet transfusion recipients prior to and following widespread blood donor SARS-CoV-2 infection and vaccination.

BACKGROUND: The safety of transfusion of SARS-CoV-2 antibodies in high plasma volume blood components to recipients without COVID-19 is not established. We assessed whether transfusion of plasma or platelet products during periods of increasing prevalence of blood donor SARS-CoV-2 infection and vaccination was associated with changes in outcomes in hospitalized patients without COVID-19. METHODS: We conducted a retrospective cohort study of hospitalized adults who received plasma or platelet transfusions at 21 hospitals during pre-COVID-19 (3/1/2018-2/29/2020), COVID-19 pre-vaccine (3/1/2020-2/28/2021), and COVID-19 post-vaccine (3/1/2021-8/31/2022) study periods. We used multivariable logistic regression with generalized estimating equations to adjust for demographics and comorbidities to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Among 21,750 hospitalizations of 18,584 transfusion recipients without COVID-19, there were 697 post-transfusion thrombotic events, and oxygen requirements were increased in 1751 hospitalizations. Intensive care unit length of stay (n = 11,683) was 3 days (interquartile range 1-5), hospital mortality occurred in 3223 (14.8%), and 30-day rehospitalization in 4144 (23.7%). Comparing the pre-COVID, pre-vaccine and post-vaccine study periods, there were no trends in thromboses (OR 0.9 [95% CI 0.8, 1.1]; p = .22) or oxygen requirements (OR 1.0 [95% CI 0.9, 1.1]; p = .41). In parallel, there were no trends across study periods for ICU length of stay (p = .83), adjusted hospital mortality (OR 1.0 [95% CI 0.9-1.0]; p = .36), or 30-day rehospitalization (p = .29). DISCUSSION: Transfusion of plasma and platelet blood components collected during the pre-vaccine and post-vaccine periods of the COVID-19 pandemic was not associated with increased adverse outcomes in transfusion recipients without COVID-19.

Development and Validation of an Electronic Health Record-based Score for Triage to Perioperative Medicine.

OBJECTIVE: To develop an electronic health record-based risk model for perioperative medicine (POM) triage and compare this model with legacy triage practices that were based on clinician assessment. SUMMARY OF BACKGROUND DATA: POM clinicians seek to address the increasingly complex medical needs of patients prior to scheduled surgery. Identifying which patients might derive the most benefit from evaluation is challenging. METHODS: Elective surgical cases performed within a health system 2014- 2019 (N = 470,727) were used to develop a predictive score, called the Comorbidity Assessment for Surgical Triage (CAST) score, using split validation. CAST incorporates patient and surgical case characteristics to predict the risk of 30-day post-operative morbidity, defined as a composite of mortality and major NSQIP complications. Thresholds of CAST were then selected to define risk groups, which correspond with triage to POM appointments of different durations and modalities. The predictive discrimination CAST score was compared with the surgeon's assessments of patient complexity and the American Society of Anesthesiologists class. RESULTS: The CAST score demonstrated a significantly higher discrimination for predicting post-operative morbidity (area under the receiver operating characteristic curve 0.75) than the surgeon's complexity designation (0.63; P < 0.001) or the American Society of Anesthesiologists (0.65; P < 0.001) ( Fig. 1 ). Incorporating the complexity designation in the CAST model did not significantly alter the discrimination (0.75; P = 0.098). Compared with the complexity designation, classification based on CAST score groups resulted a net reclassification improvement index of 10.4% ( P < 0.001) ( Table 1 ). CONCLUSION: A parsimonious electronic health record-based predictive model demonstrates improved performance for identifying pre-surgical patients who are at risk than previously-used assessments for POM triage.

Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration.

BACKGROUND: Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported. METHODS: On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions. RESULTS: The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001). CONCLUSIONS: The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).

Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study.

Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.

Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2.

BACKGROUND: Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes. OBJECTIVE: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. RESEARCH DESIGN: Retrospective cohort study. PATIENTS: ICU patients in 5 hospitals from October 2017 through September 2019. MEASURES: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c -statistics, and calibration plots. RESULTS: The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119-0.235; c -statistic: 0.772-0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109-0.175; c -statistic: 0.768-0.867) and patient-day-level (SBS: 0.064-0.153; c -statistic: 0.714-0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone. CONCLUSIONS: Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.

COVID-19 bacteremic co-infection is a major risk factor for mortality, ICU admission, and mechanical ventilation.

BACKGROUND: Recent single-center reports have suggested that community-acquired bacteremic co-infection in the context of Coronavirus disease 2019 (COVID-19) may be an important driver of mortality; however, these reports have not been validated with a multicenter, demographically diverse, cohort study with data spanning the pandemic. METHODS: In this multicenter, retrospective cohort study, inpatient encounters were assessed for COVID-19 with community-acquired bacteremic co-infection using 48-h post-admission blood cultures and grouped by: (1) confirmed co-infection [recovery of bacterial pathogen], (2) suspected co-infection [negative culture with ≥ 2 antimicrobials administered], and (3) no evidence of co-infection [no culture]. The primary outcomes were in-hospital mortality, ICU admission, and mechanical ventilation. COVID-19 bacterial co-infection risk factors and impact on primary outcomes were determined using multivariate logistic regressions and expressed as adjusted odds ratios with 95% confidence intervals (Cohort, OR 95% CI, Wald test p value). RESULTS: The studied cohorts included 13,781 COVID-19 inpatient encounters from 2020 to 2022 in the University of Alabama at Birmingham (UAB, n = 4075) and Ochsner Louisiana State University Health-Shreveport (OLHS, n = 9706) cohorts with confirmed (2.5%), suspected (46%), or no community-acquired bacterial co-infection (51.5%) and a comparison cohort consisting of 99,170 inpatient encounters from 2010 to 2019 (UAB pre-COVID-19 pandemic cohort). Significantly increased likelihood of COVID-19 bacterial co-infection was observed in patients with elevated ≥ 15 neutrophil-to-lymphocyte ratio (UAB: 1.95 [1.21-3.07]; OLHS: 3.65 [2.66-5.05], p < 0.001 for both) within 48-h of hospital admission. Bacterial co-infection was found to confer the greatest increased risk for in-hospital mortality (UAB: 3.07 [2.42-5.46]; OLHS: 4.05 [2.29-6.97], p < 0.001 for both), ICU admission (UAB: 4.47 [2.87-7.09], OLHS: 2.65 [2.00-3.48], p < 0.001 for both), and mechanical ventilation (UAB: 3.84 [2.21-6.12]; OLHS: 2.75 [1.87-3.92], p < 0.001 for both) across both cohorts, as compared to other risk factors for severe disease. Observed mortality in COVID-19 bacterial co-infection (24%) dramatically exceeds the mortality rate associated with community-acquired bacteremia in pre-COVID-19 pandemic inpatients (5.9%) and was consistent across alpha, delta, and omicron SARS-CoV-2 variants. CONCLUSIONS: Elevated neutrophil-to-lymphocyte ratio is a prognostic indicator of COVID-19 bacterial co-infection within 48-h of admission. Community-acquired bacterial co-infection, as defined by blood culture-positive results, confers greater increased risk of in-hospital mortality, ICU admission, and mechanical ventilation than previously described risk factors (advanced age, select comorbidities, male sex) for COVID-19 mortality, and is independent of SARS-CoV-2 variant.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

COVID-19 Vaccination and the Timing of Surgery Following COVID-19 Infection.

OBJECTIVE: To evaluate whether COVID-19 vaccination status or mode of anesthesia modified the temporal harms associated with surgery following coronavirus disease-2019 (COVID-19) infection. BACKGROUND: Surgery shortly after COVID-19 infection is associated with higher rates of complications, leading to recommendations to delay surgery following COVID-19 infection when possible. However, prior studies were based on populations with low or no prevalence of vaccination. METHODS: A retrospective cohort study of patients who underwent scheduled surgery in a health system from January 1, 2018 to February 28, 2022 (N=228,913) was performed. Patients were grouped by time of surgery relative to COVID-19 test positivity: 0 to 4 weeks after COVID-19 ("early post-COVID-19"), 4 to 8 weeks after COVID-19 ("mid post-COVID-19"), >8 weeks after COVID-19 ("late post-COVID-19"), surgery at least 30 days before subsequent COVID-19 ("pre-COVID-19"), and surgery with no prior or subsequent test positivity for COVID-19. RESULTS: Among patients who were not fully vaccinated at the time of COVID-19 infection, the adjusted rate of perioperative complications for the early post-COVID-19 group was significantly higher than for the pre-COVID-19 group (relative risk: 1.55; P =0.05). No significantly higher risk was identified between these groups for patients who were fully vaccinated (0.66; P =1.00), or for patients who were not fully vaccinated and underwent surgery without general anesthesia (0.52; P =0.83). CONCLUSIONS: Surgery shortly following COVID-19 infection was not associated with higher risks among fully vaccinated patients or among patients who underwent surgery without general anesthesia. Further research will be valuable to understand additional factors that modify perioperative risks associated with prior COVID-19 infection.

Adults hospitalized with breakthrough COVID-19 have lower mortality than matched unvaccinated adults.

BACKGROUND: Coronavirus disease 2019 (COVID-19) breakthrough infections are common. OBJECTIVE: Evaluate in-hospital mortality of patients with COVID-19 by vaccination status using retrospective cohort study. METHODS: We generated propensity scores for receipt of full vaccination in adults requiring supplemental oxygen hospitalized at Kaiser Permanente Northern California (1 April 2021 to 30 November 2021) with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction tests. Optimal matching of fully vaccinated/unvaccinated patients was performed comparing in-hospital mortality. RESULTS: Of 7305 patients, 1463 (20.0%) were full, 138 (1.9%) were partial, and 5704 (78.1%) were unvaccinated. Fully vaccinated were older than partial or unvaccinated (71.0, 63.0, and 54.0 years, respectively, p < 0.001) with more comorbidities (Comorbidity Point Scores 33.0, 22.0, and 10.0, p < 0.001) and immunosuppressant (11.5%, 8.7%, and 3.0%, p < 0.001) or chemotherapy exposure (2.8%, 0.7%, and 0.4%, p < 0.001). Fewer fully vaccinated patients died compared to matched unvaccinated (9.0% vs. 16.3%, p < 0.0001). CONCLUSION: Fully vaccinated patients are less likely to die compared to matched unvaccinated patients.

Validation of Respiratory Rate-Oxygenation Index in Patients With COVID-19-Related Respiratory Failure.

OBJECTIVES: The respiratory rate-oxygenation (ROX) index is a fraction of oxygen saturation, Fio2, and respiratory rate that has been validated to predict receipt of invasive mechanical ventilation in patients receiving high-flow nasal cannula (HFNC). This study aimed to validate ROX in a cohort of inpatients with COVID-19-related respiratory failure. DESIGN: Retrospective validation of the ROX index. We calculated sensitivity, specificity, positive predictive value, negative predictive value, and 95% CIs of ROX for invasive mechanical ventilation any time during hospitalization. SETTING: Twenty-one hospitals of Kaiser Permanente Northern California, an integrated healthcare delivery system. PATIENTS: We identified adults with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction test within 3 weeks of, or during, hospitalization between February 1, 2020, and December 31, 2020. We calculated ROX at 12 hours after HFNC initiation. We grouped patients as low (≥ 4.88), intermediate (< 4.88 and ≥ 3.85), or high (< 3.85) risk using previously published thresholds. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 1,847 patients who had no limitation of life support. Of these, 525 (31.7%) received invasive mechanical ventilation any time during hospitalization and 511 died (27.7%). The sensitivity, specificity, positive predictive value, and negative predictive value of 12-hour ROX threshold (< 3.85) predicting invasive mechanical ventilation were 32.3% (95% CI, 28.5-36.3%), 89.8% (95% CI, 88.0-91.4%), 59.4% (95% CI, 53.8-64.9%), and 74.1% (95% CI, 71.8-76.3%), respectively. CONCLUSIONS: The 12-hour ROX index has a positive predictive value (59.4%) using threshold of less than 3.85 for COVID-19 patients needing invasive mechanical ventilation. Our health system has embedded ROX into the electronic health record to prioritize rounding during periods of inpatient surge.

Unsupervised probabilistic models for sequential Electronic Health Records.

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.

New complementary perspectives for inpatient physical function assessment: matched clinician-report and patient-report short form measures from the PROMIS adult physical function item bank.

PURPOSE: To develop two item content-matched, precise, score-level targeted inpatient physical function (PF) short form (SF) measures: one clinician-reported, one patient-reported. Items were derived from PROMIS PF bank content; scores are reported on the PROMIS PF T-score metric. METHODS: The PROMIS PF item bank was reviewed for content measuring lower-level PF status (T-scores 10-50) with high item set score-level reliability (≥ 0.90). Selected patient-reported (PR) items were also edited to function as clinician-reported (CR) items. Items were reviewed by clinicians and field tested; responses were assessed for meeting PROMIS measure development standards. New CR and PR items were calibrated using patient responses to the original PROMIS PF items as anchoring data. SFs were constructed, based on content and precision. RESULTS: Nine PROMIS PF items were candidates for CR and PR inpatient PF assessment; three new items were written to extend content coverage. An inpatient sample (N = 515; 55.1% female; mean age = 66.2 years) completed 12 PR items and was assessed by physical therapists (using 12 CR items). Analyses indicated item sets met expected measure development standards. Twelve new CR and three new PR items were linked to the PROMIS PF metric (raw score r = 0.73 and 0.90, respectively). A 5-item CR SF measure was constructed; score-level reliabilities were ≥ 0.90 for T-scores 13-45. A 5-item PR SF measure was assembled, mirroring CR SF content. CONCLUSIONS: Two item content-matched SFs have been developed for clinician and patient reporting and are an effective, efficient means of assessing inpatient PF and offer complementary perspectives.

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives: To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. Methods: We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Measurements and Main Results: Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Conclusions: Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.

Association between Troponin I Levels during Sepsis and Postsepsis Cardiovascular Complications.

Rationale: Sepsis commonly results in elevated serum troponin levels and increased risk for postsepsis cardiovascular complications; however, the association between troponin levels during sepsis and cardiovascular complications after sepsis is unclear.Objectives: To evaluate the association between serum troponin levels during sepsis and 1 year after sepsis cardiovascular events.Methods: We analyzed adults aged ⩾40 years without preexisting cardiovascular disease within 5 years, admitted with sepsis across 21 hospitals from 2011 to 2017. Peak serum troponin I levels during sepsis were grouped as normal (⩽0.04 ng/ml) or tertiles of abnormal (>0.04 to ⩽0.09 ng/ml, >0.09 to ⩽0.42 ng/ml, or >0.42 ng/ml). Multivariable adjusted cause-specific Cox proportional hazards models with death as a competing risk were used to assess associations between peak troponin I levels and a composite cardiovascular outcome (atherosclerotic cardiovascular disease, atrial fibrillation, and heart failure) in the year following sepsis. Models were adjusted for presepsis and intrasepsis factors considered potential confounders.Measurements and Main Results: Among 14,046 eligible adults with troponin I measured, 2,012 (14.3%) experienced the composite cardiovascular outcome, including 832 (10.9%) patients with normal troponin levels, as compared with 370 (17.3%), 376 (17.6%), and 434 (20.3%) patients within each sequential abnormal troponin tertile, respectively (P < 0.001). Patients within the elevated troponin tertiles had increased risks of adverse cardiovascular events (adjusted hazard ratio [aHR]troponin0.04-0.09 = 1.37; 95% confidence interval [CI], 1.20-1.55; aHRtroponin0.09-0.42 = 1.44; 95% CI, 1.27-1.63; and aHRtroponin>0.42 = 1.77; 95% CI, 1.56-2.00).Conclusions: Among patients without preexisting cardiovascular disease, troponin elevation during sepsis identified patients at increased risk for postsepsis cardiovascular complications. Strategies to mitigate cardiovascular complications among this high-risk subset of patients are warranted.

Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality.

BACKGROUND: Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone. METHODS: We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation. RESULTS: The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors). CONCLUSIONS: In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.

Racial Disparities in COVID-19 Testing and Outcomes : Retrospective Cohort Study in an Integrated Health System.

BACKGROUND: Racial disparities exist in outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. OBJECTIVE: To evaluate the contribution of race/ethnicity in SARS-CoV-2 testing, infection, and outcomes. DESIGN: Retrospective cohort study (1 February 2020 to 31 May 2020). SETTING: Integrated health care delivery system in Northern California. PARTICIPANTS: Adult health plan members. MEASUREMENTS: Age, sex, neighborhood deprivation index, comorbid conditions, acute physiology indices, and race/ethnicity; SARS-CoV-2 testing and incidence of positive test results; and hospitalization, illness severity, and mortality. RESULTS: Among 3 481 716 eligible members, 42.0% were White, 6.4% African American, 19.9% Hispanic, and 18.6% Asian; 13.0% were of other or unknown race. Of eligible members, 91 212 (2.6%) were tested for SARS-CoV-2 infection and 3686 had positive results (overall incidence, 105.9 per 100 000 persons; by racial group, White, 55.1; African American, 123.1; Hispanic, 219.6; Asian, 111.7; other/unknown, 79.3). African American persons had the highest unadjusted testing and mortality rates, White persons had the lowest testing rates, and those with other or unknown race had the lowest mortality rates. Compared with White persons, adjusted testing rates among non-White persons were marginally higher, but infection rates were significantly higher; adjusted odds ratios [aORs] for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 2.01 (95% CI, 1.75 to 2.31), 3.93 (CI, 3.59 to 4.30), 2.19 (CI, 1.98 to 2.42), and 1.57 (CI, 1.38 to 1.78), respectively. Geographic analyses showed that infections clustered in areas with higher proportions of non-White persons. Compared with White persons, adjusted hospitalization rates for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 1.47 (CI, 1.03 to 2.09), 1.42 (CI, 1.11 to 1.82), 1.47 (CI, 1.13 to 1.92), and 1.03 (CI, 0.72 to 1.46), respectively. Adjusted analyses showed no racial differences in inpatient mortality or total mortality during the study period. For testing, comorbid conditions made the greatest relative contribution to model explanatory power (77.9%); race only accounted for 8.1%. Likelihood of infection was largely due to race (80.3%). For other outcomes, age was most important; race only contributed 4.5% for hospitalization, 12.8% for admission illness severity, 2.3% for in-hospital death, and 0.4% for any death. LIMITATION: The study involved an insured population in a highly integrated health system. CONCLUSION: Race was the most important predictor of SARS-CoV-2 infection. After infection, race was associated with increased hospitalization risk but not mortality. PRIMARY FUNDING SOURCE: The Permanente Medical Group, Inc.

Association Between Preoperative Hemodialysis Timing and Postoperative Mortality in Patients With End-stage Kidney Disease.

Importance: For patients with end-stage kidney disease treated with hemodialysis, the optimal timing of hemodialysis prior to elective surgical procedures is unknown. Objective: To assess whether a longer interval between hemodialysis and subsequent surgery is associated with higher postoperative mortality in patients with end-stage kidney disease treated with hemodialysis. Design, Setting, and Participants: Retrospective cohort study of 1 147 846 procedures among 346 828 Medicare beneficiaries with end-stage kidney disease treated with hemodialysis who underwent surgical procedures between January 1, 2011, and September 30, 2018. Follow-up ended on December 31, 2018. Exposures: One-, two-, or three-day intervals between the most recent hemodialysis treatment and the surgical procedure. Hemodialysis on the day of the surgical procedure vs no hemodialysis on the day of the surgical procedure. Main Outcomes and Measures: The primary outcome was 90-day postoperative mortality. The relationship between the dialysis-to-procedure interval and the primary outcome was modeled using a Cox proportional hazards model. Results: Of the 1 147 846 surgical procedures among 346 828 patients (median age, 65 years [IQR, 56-73 years]; 495 126 procedures [43.1%] in female patients), 750 163 (65.4%) were performed when the last hemodialysis session occurred 1 day prior to surgery, 285 939 (24.9%) when the last hemodialysis session occurred 2 days prior to surgery, and 111 744 (9.7%) when the last hemodialysis session occurred 3 days prior to surgery. Hemodialysis was also performed on the day of surgery for 193 277 procedures (16.8%). Ninety-day postoperative mortality occurred after 34 944 procedures (3.0%). Longer intervals between the last hemodialysis session and surgery were significantly associated with higher risk of 90-day mortality in a dose-dependent manner (2 days vs 1 day: absolute risk, 4.7% vs 4.2%, absolute risk difference, 0.6% [95% CI, 0.4% to 0.8%], adjusted hazard ratio [HR], 1.14 [95% CI, 1.10 to 1.18]; 3 days vs 1 day: absolute risk, 5.2% vs 4.2%, absolute risk difference, 1.0% [95% CI, 0.8% to 1.2%], adjusted HR, 1.25 [95% CI, 1.19 to 1.31]; and 3 days vs 2 days: absolute risk, 5.2% vs 4.7%, absolute risk difference, 0.4% [95% CI, 0.2% to 0.6%], adjusted HR, 1.09 [95% CI, 1.04 to 1.13]). Undergoing hemodialysis on the same day as surgery was associated with a significantly lower hazard of mortality vs no same-day hemodialysis (absolute risk, 4.0% for same-day hemodialysis vs 4.5% for no same-day hemodialysis; absolute risk difference, -0.5% [95% CI, -0.7% to -0.3%]; adjusted HR, 0.88 [95% CI, 0.84-0.91]). In the analyses that evaluated the interaction between the hemodialysis-to-procedure interval and same-day hemodialysis, undergoing hemodialysis on the day of the procedure significantly attenuated the risk associated with a longer hemodialysis-to-procedure interval (P<.001 for interaction). Conclusions and Relevance: Among Medicare beneficiaries with end-stage kidney disease, longer intervals between hemodialysis and surgery were significantly associated with higher risk of postoperative mortality, mainly among those who did not receive hemodialysis on the day of surgery. However, the magnitude of the absolute risk differences was small, and the findings are susceptible to residual confounding.

Effect of donor, component, and recipient characteristics on hemoglobin increments following red blood cell transfusion.

Significant research has focused individually on blood donors, product preparation and storage, and optimal transfusion practice. To better understand the interplay between these factors on measures of red blood cell (RBC) transfusion efficacy, we conducted a linked analysis of blood donor and component data with patients who received single-unit RBC transfusions between 2008 and 2016. Hemoglobin levels before and after RBC transfusions and at 24- and 48-hour intervals after transfusion were analyzed. Generalized estimating equation linear regression models were fit to examine hemoglobin increments after RBC transfusion adjusting for donor and recipient demographic characteristics, collection method, additive solution, gamma irradiation, and storage duration. We linked data on 23 194 transfusion recipients who received one or more single-unit RBC transfusions (n = 38 019 units) to donor demographic and component characteristics. Donor and recipient sex, Rh-D status, collection method, gamma irradiation, recipient age and body mass index, and pretransfusion hemoglobin levels were significant predictors of hemoglobin increments in univariate and multivariable analyses (P < .01). For hemoglobin increments 24 hours after transfusion, the coefficient of determination for the generalized estimating equation models was 0.25, with an estimated correlation between actual and predicted values of 0.5. Collectively, blood donor demographic characteristics, collection and processing methods, and recipient characteristics accounted for significant variation in hemoglobin increments related to RBC transfusion. Multivariable modeling allows the prediction of changes in hemoglobin using donor-, component-, and patient-level characteristics. Accounting for these factors will be critical for future analyses of donor and component factors, including genetic polymorphisms, on posttransfusion increments and other patient outcomes.