Video-Assisted vs Robotic-Assisted Lung Lobectomies for Operating Room Resource Utilization and Patient Outcomes.

Importance: With increased use of robots, there is an inadequate understanding of minimally invasive modalities' time costs. This study evaluates the operative durations of robotic-assisted vs video-assisted lung lobectomies. Objective: To compare resource utilization, specifically operative time, between video-assisted and robotic-assisted thoracoscopic lung lobectomies. Design, Setting, and Participants: This retrospective cohort study evaluated patients aged 18 to 90 years who underwent minimally invasive (robotic-assisted or video-assisted) lung lobectomy from January 1, 2020, to December 31, 2022, with 90 days' follow-up after surgery. The study included multicenter electronic health record data from 21 hospitals within an integrated health care system in Northern California. Thoracic surgery was regionalized to 4 centers with 14 board-certified general thoracic surgeons. Exposures: Robotic-assisted or video-assisted lung lobectomy. Main Outcomes and Measures: The primary outcome was operative duration (cut to close) in minutes. Secondary outcomes were length of stay, 30-day readmission, and 90-day mortality. Comparisons between video-assisted and robotic-assisted lobectomies were generated using the Wilcoxon rank sum test for continuous variables and the χ2 test for categorical variables. The average treatment effects were estimated with augmented inverse probability treatment weighting (AIPTW). Patient and surgeon covariates were adjusted for and included patient demographics, comorbidities, and case complexity (age, sex, race and ethnicity, neighborhood deprivation index, body mass index, Charlson Comorbidity Index score, nonelective hospitalizations, emergency department visits, a validated laboratory derangement score, a validated institutional comorbidity score, a surgeon-designated complexity indicator, and a procedural code count), and a primary surgeon-specific indicator. Results: The study included 1088 patients (median age, 70.1 years [IQR, 63.3-75.8 years]; 704 [64.7%] female), of whom 446 (41.0%) underwent robotic-assisted and 642 (59.0%) underwent video-assisted lobectomy. The median unadjusted operative duration was 172.0 minutes (IQR, 128.0-226.0 minutes). After AIPTW, there was less than a 10% difference in all covariates between groups, and operative duration was a median 20.6 minutes (95% CI, 12.9-28.2 minutes; P < .001) longer for robotic-assisted compared with video-assisted lobectomies. There was no difference in adjusted secondary patient outcomes, specifically for length of stay (0.3 days; 95% CI, -0.3 to 0.8 days; P = .11) or risk of 30-day readmission (adjusted odds ratio, 1.29; 95% CI, 0.84-1.98; P = .13). The unadjusted 90-day mortality rate (1.3% [n = 14]) was too low for the AIPTW modeling process. Conclusions and Relevance: In this cohort study, there was no difference in patient outcomes between modalities, but operative duration was longer in robotic-assisted compared with video-assisted lung lobectomy. Given that this elevated operative duration is additive when applied systematically, increased consideration of appropriate patient selection for robotic-assisted lung lobectomy is needed to improve resource utilization.

Predicting stroke risk after sepsis hospitalization with new-onset atrial fibrillation.

BACKGROUND: New-onset atrial fibrillation (AF) during sepsis is common, but models designed to stratify stroke risk excluded patients with secondary AF. We assessed the predictive validity of CHA2DS2VASc scores among patients with new-onset AF during sepsis and developed a novel stroke prediction model incorporating presepsis and intrasepsis characteristics. METHODS: We included patients ≥40 years old who survived hospitalizations with sepsis and new-onset AF across 21 Kaiser Permanente Northern California hospitals from January 1, 2011 to September 30, 2017. We calculated the area under the receiver operating curve (AUC) for CHA2DS2VASc scores to predict stroke or transient ischemic attack (TIA) within 1 year after a hospitalization with new-onset AF during sepsis using Fine-Gray models with death as competing risk. We similarly derived and validated a novel model using presepsis and intrasepsis characteristics associated with 1-year stroke/TIA risk. RESULTS: Among 82,748 adults hospitalized with sepsis, 3992 with new-onset AF (median age: 80 years, median CHA2DS2VASc of 4) survived to discharge, among whom 70 (2.1%) experienced stroke or TIA outcome and 1393 (41.0%) died within 1 year of sepsis. The CHA2DS2VASc score was not predictive of stroke risk after sepsis (AUC: 0.50, 95% confidence interval [CI]: 0.48-0.52). A newly derived model among 2555 (64%) patients in the derivation set and 1437 (36%) in the validation set included 13 variables and produced an AUC of 0.61 (0.49-0.73) in derivation and 0.54 (0.43-0.65) in validation. CONCLUSION: Current models do not accurately stratify risk of stroke following new-onset AF secondary to sepsis. New tools are required to guide anticoagulation decisions following new-onset AF in sepsis.

Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery.

Importance: Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. Objective: To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. Design, Setting, and Participants: This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Exposure: Body composition derived from automated analysis of multislice abdominal CT scans. Main Outcomes and Measures: The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. Results: The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. Conclusions and Relevance: In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.

Importance: Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective: To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants: This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures: Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results: The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance: In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

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.

Impact of Reducing Time-to-Antibiotics on Sepsis Mortality, Antibiotic Use, and Adverse Events.

Rationale: Shorter time-to-antibiotics is lifesaving in sepsis, but programs to hasten antibiotic delivery may increase unnecessary antibiotic use and adverse events. Objectives: We sought to estimate both the benefits and harms of shortening time-to-antibiotics for sepsis. Methods: We conducted a simulation study using a cohort of 1,559,523 hospitalized patients admitted through the emergency department with meeting two or more systemic inflammatory response syndrome criteria (2013-2018). Reasons for hospitalization were classified as septic shock, sepsis, infection, antibiotics stopped early, and never treated (no antibiotics within 48 h). We simulated the impact of a 50% reduction in time-to-antibiotics for sepsis across 12 hospital scenarios defined by sepsis prevalence (low, medium, or high) and magnitude of "spillover" antibiotic prescribing to patients without infection (low, medium, high, or very high). Outcomes included mortality and adverse events potentially attributable to antibiotics (e.g., allergy, organ dysfunction, Clostridiodes difficile infection, and culture with multidrug-resistant organism). Results: A total of 933,458 (59.9%) hospitalized patients received antimicrobial therapy within 48 hours of presentation, including 38,572 (2.5%) with septic shock, 276,082 (17.7%) with sepsis, 370,705 (23.8%) with infection, and 248,099 (15.9%) with antibiotics stopped early. A total of 199,937 (12.8%) hospitalized patients experienced an adverse event; most commonly, acute liver injury (5.6%), new MDRO (3.5%), and Clostridiodes difficile infection (1.7%). Across the scenarios, a 50% reduction in time-to-antibiotics for sepsis was associated with a median of 1 to 180 additional antibiotic-treated patients and zero to seven additional adverse events per death averted from sepsis. Conclusions: The impacts of faster time-to-antibiotics for sepsis vary markedly across simulated hospital types. However, even in the worst-case scenario, new antibiotic-associated adverse events were rare.

Identifying Optimal Acute Care Comparators to Inform the Evaluation of an Advanced Care at Home Pilot Program.

BACKGROUND: Hospital at Home (H@H) programs-which seek to deliver acute care within a patient's home-have become more prevalent over time. However, existing literature exhibits heterogeneity in program structure, evaluation design, and target population size, making it difficult to draw generalizable conclusions to inform future H@H program design. OBJECTIVE: The objective of this work was to develop a quality improvement evaluation strategy for a H@H program-the Kaiser Permanente Advanced Care at Home (KPACAH) program in Northern California-leveraging electronic health record data, chart review, and patient surveys to compare KPACAH patients with inpatients in traditional hospital settings. METHODS: The authors developed a 3-step recruitment workflow that used electronic health record filtering tools to generate a daily list of potential comparators, a manual chart review of potentially eligible comparator patients to assess individual clinical and social criteria, and a phone interview with patients to affirm eligibility and interest from potential comparator patients. RESULTS: This workflow successfully identified and enrolled a population of 446 comparator patients in a 5-month period who exhibited similar demographics, reasons for hospitalization, comorbidity burden, and utilization measures to patients enrolled in the KPACAH program. CONCLUSION: These initial findings provide promise for a workflow that can facilitate the identification of similar inpatients hospitalized at traditional brick and mortar facilities to enhance outcomes evaluations for the H@H programs, as well as to identify the potential volume of enrollees as the program expands.

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.

The impact of timing of initiating invasive mechanical ventilation in COVID-19-related respiratory failure.

PURPOSE: Optimal timing of initiating invasive mechanical ventilation (IMV) in coronavirus disease 2019 (COVID-19)-related respiratory failure is unclear. We hypothesized that a strategy of IMV as opposed to continuing high flow oxygen or non-invasive mechanical ventilation each day after reaching a high FiO2 threshold would be associated with worse in-hospital mortality. METHODS: Using data from Kaiser Permanente Northern/Southern California's 36 medical centers, we identified patients with COVID-19-related acute respiratory failure who reached ≥80% FiO2 on high flow nasal cannula or non-invasive ventilation. Exposure was IMV initiation each day after reaching high FiO2 threshold (T0). We developed propensity scores with overlap weighting for receipt of IMV each day adjusting for confounders. We reported relative risk of inpatient death with 95% Confidence Interval. RESULTS: Of 28,035 hospitalizations representing 21,175 patient-days, 5758 patients were included (2793 received and 2965 did not receive IMV). Patients receiving IMV had higher unadjusted mortality (63.6% versus 18.2%, P < 0.0001). On each day after reaching T0 through day >10, the adjusted relative risk was higher for those receiving IMV compared to those not receiving IMV (Relative Risk>1). CONCLUSIONS: Initiation of IMV on each day after patients reach high FiO2 threshold was associated with higher inpatient mortality after adjusting for time-varying confounders. Remaining on high flow nasal cannula or non-invasive ventilation does not appear to be harmful compared to IMV. Prospective evaluation is needed.

Performance of a Prediction Model of Suicide Attempts Across Race and Ethnicity.

This study examines whether race disparities exist in the prediction of suicide attempts and if have they have detrimental effects on individuals and health care systems.

Practice Patterns and Outcomes Associated With Anticoagulation Use Following Sepsis Hospitalizations With New-Onset Atrial Fibrillation.

BACKGROUND: Practice patterns and outcomes associated with the use of oral anticoagulation for arterial thromboembolism prevention following a hospitalization with new-onset atrial fibrillation (AF) during sepsis are unclear. METHODS: Retrospective, observational cohort study of patients ≥40 years of age discharged alive following hospitalization with new-onset AF during sepsis across 21 hospitals in the Kaiser Permanente Northern California health care delivery system, years 2011 to 2018. Primary outcomes were ischemic stroke/transient ischemic attack (TIA), with a safety outcome of major bleeding events, both within 1 year of discharge alive from sepsis hospitalization. Adjusted risk differences for outcomes between patients who did and did not receive oral anticoagulation within 30 days of discharge were estimated using marginal structural models fitted by inverse probability weighting using Super Learning within a target trial emulation framework. RESULTS: Among 82 748 patients hospitalized with sepsis, 3992 (4.8%) had new-onset AF and survived to hospital discharge; mean age was 78±11 years, 53% were men, and 70% were White. Patients with new-onset AF during sepsis averaged 45±33% of telemetry monitoring entries with AF, and 27% had AF present on the day of hospital discharge. Within 1 year of hospital discharge, 89 (2.2%) patients experienced stroke/TIA, 225 (5.6%) had major bleeding, and 1011 (25%) died. Within 30 days of discharge, 807 (20%) patients filled oral anticoagulation prescriptions, which were associated with higher 1-year adjusted risks of ischemic stroke/TIA (5.69% versus 2.32%; risk difference, 3.37% [95% CI, 0.36-6.38]) and no significant difference in 1-year adjusted risks of major bleeding (6.51% versus 7.10%; risk difference, -0.59% [95% CI, -3.09 to 1.91]). Sensitivity analysis of ischemic stroke-only outcomes showed a risk difference of 0.15% (95% CI, -1.72 to 2.03). CONCLUSIONS: After hospitalization with new-onset AF during sepsis, oral anticoagulation use was uncommon and associated with potentially higher stroke/TIA risk. Further research to inform mechanisms of stroke and TIA and management of new-onset AF after sepsis is needed.

Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).

Background: Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design: Retrospective cohort study. Subjects: All ICU patients in five 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 four 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 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions: Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

Trends in Outpatient Visits and Hospital and Intensive Care Unit Admissions of Adults With COVID-19 in an Integrated US Health Care System, March 2020 to January 2022.

This cohort study of patients at a single integrated health system examines trends in COVID-19­related treatment location and mortality.

Risk of hospitalization in a sample of COVID-19 patients with and without chronic obstructive pulmonary disease.

BACKGROUND AND OBJECTIVE: Patients with chronic obstructive pulmonary disease (COPD) may have worse coronavirus disease-2019 (COVID-19)-related outcomes. We compared COVID-19 hospitalization risk in patients with and without COPD. METHODS: This retrospective cohort study included patients ≥40 years, SARS-CoV-2 positive, and with Kaiser Permanente Northern California membership ≥1 year before COVID-19 diagnosis (electronic health records and claims data). COVID-19-related hospitalization risk was assessed by sequentially adjusted logistic regression models and stratified by disease severity. Secondary outcome was death/hospice referral after COVID-19. RESULTS AND DISCUSSION: Of 19,558 COVID-19 patients, 697 (3.6%) had COPD. Compared with patients without COPD, COPD patients were older (median age: 69 vs 53 years); had higher Elixhauser Comorbidity Index (5 vs 0) and more median baseline outpatient (8 vs 4), emergency department (2 vs 1), and inpatient (2 vs 1) encounters. Unadjusted analyses showed increased odds of hospitalization with COPD (odds ratio [OR]: 3.93; 95% confidence interval [CI]: 3.40-4.60). After full risk adjustment, there were no differences in odds of hospitalization (OR: 1.14, 95% CI: 0.93-1.40) or death/hospice referral (OR: 0.96, 95% CI: 0.72-1.27) between patients with and without COPD. Primary/secondary outcomes did not differ by COPD severity, except for higher odds of hospitalization in COPD patients requiring supplemental oxygen versus those without COPD (OR: 1.84, 95% CI: 1.02-3.33). CONCLUSIONS: Except for hospitalization among patients using supplemental oxygen, no differences in odds of hospitalization or death/hospice referral were observed in the COVID-19 patient sample depending on whether they had COPD.

Persistent Steroid Exposure Before Coronavirus Disease 2019 Diagnosis and Risk of Hospitalization in Patients With Chronic Obstructive Pulmonary Disease.

Background: It is unclear whether persistent inhaled steroid exposure in chronic obstructive pulmonary disease (COPD) patients before coronavirus disease 2019 (COVID-19) is associated with hospitalization risk. Objective: Our objective was to examine the association between persistent steroid exposure and COVID-19-related hospitalization risk in COPD patients. Study Design and Methods: This retrospective cohort study used electronic health records from the Kaiser Permanente Northern California health care system (February 2, 2020, to September 30, 2020) for patients aged ≥40 years with COPD and a positive polymerase chain reaction test result for COVID-19. Primary exposure was persistent oral and/or inhaled steroid exposure defined as ≥6 months of prescriptions filled in the year before the COVID-19 diagnosis. Multivariable logistic regression was performed for the primary outcome of COVID-19-related hospitalization or death/hospice referral. Steroid exposure in the month before a COVID-19 diagnosis was a covariate. Results: Of >4.3 million adults, 697 had COVID-19 and COPD, of whom 270 (38.7%) had COVID-19-related hospitalizations. Overall, 538 (77.2%) were neither exposed to steroids in the month before COVID-19 diagnosis nor persistently exposed; 53 (7.6%) were exposed in the month before but not persistently; 23 (3.3%) were exposed persistently but not in the month before; and 83 (11.9%) were exposed both persistently and in the month before. Adjusting for all confounders including steroid use in the month before, the odds ratio for hospitalization was 0.77 (95% confidence interval 0.41-1.46) for patients persistently exposed to steroids before a COVID-19 diagnosis. Interpretation: No association was observed between persistent steroid exposure and the risk of COVID-19-related hospitalization in COPD patients.

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.

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.

Temporal Trends in Antimicrobial Prescribing During Hospitalization for Potential Infection and Sepsis.

Importance: Some experts have cautioned that national and health system emphasis on rapid administration of antimicrobials for sepsis may increase overall antimicrobial use even among patients without sepsis. Objective: To assess whether temporal changes in antimicrobial timing for sepsis are associated with increasing antimicrobial use, days of therapy, or broadness of antimicrobial coverage among all hospitalized patients at risk for sepsis. Design, Setting, and Participants: This is an observational cohort study of hospitalized patients at 152 hospitals in 2 health care systems during 2013 to 2018, admitted via the emergency department with 2 or more systemic inflammatory response syndrome (SIRS) criteria. Data analysis was performed from June 10, 2021, to March 22, 2022. Exposures: Hospital-level temporal trends in time to first antimicrobial administration. Outcomes: Antimicrobial outcomes included antimicrobial use, days of therapy, and broadness of antibacterial coverage. Clinical outcomes included in-hospital mortality, 30-day mortality, length of hospitalization, and new multidrug-resistant (MDR) organism culture positivity. Results: Among 1 559 523 patients admitted to the hospital via the emergency department with 2 or more SIRS criteria (1 269 998 male patients [81.4%]; median [IQR] age, 67 [59-77] years), 273 255 (17.5%) met objective criteria for sepsis. In multivariable models adjusted for patient characteristics, the adjusted median (IQR) time to first antimicrobial administration to patients with sepsis decreased by 37 minutes, from 4.7 (4.1-5.3) hours in 2013 to 3.9 (3.6-4.4) hours in 2018, although the slope of decrease varied across hospitals. During the same period, antimicrobial use within 48 hours, days of antimicrobial therapy, and receipt of broad-spectrum coverage decreased among the broader cohort of patients with SIRS. In-hospital mortality, 30-day mortality, length of hospitalization, new MDR culture positivity, and new MDR blood culture positivity decreased over the study period among both patients with sepsis and those with SIRS. When examining hospital-specific trends, decreases in antimicrobial use, days of therapy, and broadness of antibacterial coverage for patients with SIRS did not differ by hospital antimicrobial timing trend for sepsis. Overall, there was no evidence that accelerating antimicrobial timing for sepsis was associated with increasing antimicrobial use or impaired antimicrobial stewardship. Conclusions and Relevance: In this multihospital cohort study, the time to first antimicrobial for sepsis decreased over time, but this trend was not associated with increasing antimicrobial use, days of therapy, or broadness of antimicrobial coverage among the broader population at-risk for sepsis, which suggests that shortening the time to antibiotics for sepsis is feasible without leading to indiscriminate antimicrobial use.

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.