Dexamethasone Dose for COVID-19 in a Large U.S. Hospital Network From April 2020 to May 2023.

PURPOSE: While potential harm from high doses of systemic dexamethasone for clinical management of COVID-19 is an important concern, little is known about real world dexamethasone dosing in patients hospitalized with COVID-19 in the United States. METHODS: Descriptive study to assess dexamethasone daily dose in adults with COVID-19 in a large US hospital network, overall and by respiratory support requirements, extracted using semi- structured nursing notes. RESULTS: Of 332 430 hospitalizations with a COVID-19 diagnosis, 201 637 (60.7%) hospitalizations included dexamethasone administration. The mean age of recipients was 63 years, 53.0% were male, and 64.5% White. Median time from admission to dexamethasone administration was 0 day (interquartile range [IQR], 0-1 days) and median duration of use was 5 (IQR, 3-9) days. Almost 80% of hospitalizations received standard daily doses (≤ 6 mg daily), 12.7% moderately high daily doses (> 6- ≤ 10 mg daily), and 8.1% high (> 10- ≤ 20 mg daily) or very high daily dose (> 20 mg daily). Over 20% of COVID-19 hospitalizations requiring no oxygen or simple oxygen received high doses of systemic dexamethasone. CONCLUSIONS: Given the findings from the UK RECOVERY trial, and the general uncertainty around safety of higher dexamethasone doses in those requiring more intense respiratory support, standard daily dexamethasone doses of 6 mg or less for hospitalized COVID-19 requiring supplemental oxygen are recommended.

Validity of inpatient electronic health record-based measures of oxygen-related therapy in the United States: Lessons applicable for studying COVID-19 endpoints.

INTRODUCTION: During the COVID-19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID-19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID-19 hospitalization in administrative data versus nursing documentation. METHODS: We identified 319 553 adult hospitalizations with a COVID-19 diagnosis, February 2020-October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi-structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID-19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses. RESULTS: In nursing documentation, the prevalence of MV and O2 supplementation among COVID-19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%). CONCLUSIONS: In comparison to nursing documentation, administrative data under-ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID-19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID-19 or other respiratory illnesses.

Stewardship Prompts to Improve Antibiotic Selection for Urinary Tract Infection: The INSPIRE Randomized Clinical Trial.

Importance: Urinary tract infection (UTI) is the second most common infection leading to hospitalization and is often associated with gram-negative multidrug-resistant organisms (MDROs). Clinicians overuse extended-spectrum antibiotics although most patients are at low risk for MDRO infection. Safe strategies to limit overuse of empiric antibiotics are needed. Objective: To evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO risk estimates could reduce use of empiric extended-spectrum antibiotics for treatment of UTI. Design, Setting, and Participants: Cluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time and risk-based CPOE prompts; 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in noncritically ill adults (≥18 years) hospitalized with UTI with an 18-month baseline (April 1, 2017-September 30, 2018) and 15-month intervention period (April 1, 2019-June 30, 2020). Interventions: CPOE prompts recommending empiric standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics who have low estimated absolute risk (<10%) of MDRO UTI, coupled with feedback and education. Main Outcomes and Measures: The primary outcome was empiric (first 3 days of hospitalization) extended-spectrum antibiotic days of therapy. Secondary outcomes included empiric vancomycin and antipseudomonal days of therapy. Safety outcomes included days to intensive care unit (ICU) transfer and hospital length of stay. Outcomes were assessed using generalized linear mixed-effect models to assess differences between the baseline and intervention periods. Results: Among 127 403 adult patients (71 991 baseline and 55 412 intervention period) admitted with UTI in 59 hospitals, the mean (SD) age was 69.4 (17.9) years, 30.5% were male, and the median Elixhauser Comorbidity Index count was 4 (IQR, 2-5). Compared with routine stewardship, the group using CPOE prompts had a 17.4% (95% CI, 11.2%-23.2%) reduction in empiric extended-spectrum days of therapy (rate ratio, 0.83 [95% CI, 0.77-0.89]; P < .001). The safety outcomes of mean days to ICU transfer (6.6 vs 7.0 days) and hospital length of stay (6.3 vs 6.5 days) did not differ significantly between the routine and intervention groups, respectively. Conclusions and Relevance: Compared with routine stewardship, CPOE prompts providing real-time recommendations for standard-spectrum antibiotics for patients with low MDRO risk coupled with feedback and education significantly reduced empiric extended-spectrum antibiotic use among noncritically ill adults admitted with UTI without changing hospital length of stay or days to ICU transfers. Trial Registration: ClinicalTrials.gov Identifier: NCT03697096.

Stewardship Prompts to Improve Antibiotic Selection for Pneumonia: The INSPIRE Randomized Clinical Trial.

Importance: Pneumonia is the most common infection requiring hospitalization and is a major reason for overuse of extended-spectrum antibiotics. Despite low risk of multidrug-resistant organism (MDRO) infection, clinical uncertainty often drives initial antibiotic selection. Strategies to limit empiric antibiotic overuse for patients with pneumonia are needed. Objective: To evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO infection risk estimates could reduce empiric extended-spectrum antibiotics for non-critically ill patients admitted with pneumonia. Design, Setting, and Participants: Cluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time MDRO risk-based CPOE prompts; n = 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in non-critically ill adults (≥18 years) hospitalized with pneumonia. There was an 18-month baseline period from April 1, 2017, to September 30, 2018, and a 15-month intervention period from April 1, 2019, to June 30, 2020. Intervention: CPOE prompts recommending standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics during the empiric period who have low estimated absolute risk (<10%) of MDRO pneumonia, coupled with feedback and education. Main Outcomes and Measures: The primary outcome was empiric (first 3 days of hospitalization) extended-spectrum antibiotic days of therapy. Secondary outcomes included empiric vancomycin and antipseudomonal days of therapy and safety outcomes included days to intensive care unit (ICU) transfer and hospital length of stay. Outcomes compared differences between baseline and intervention periods across strategies. Results: Among 59 hospitals with 96 451 (51 671 in the baseline period and 44 780 in the intervention period) adult patients admitted with pneumonia, the mean (SD) age of patients was 68.1 (17.0) years, 48.1% were men, and the median (IQR) Elixhauser comorbidity count was 4 (2-6). Compared with routine stewardship, the group using CPOE prompts had a 28.4% reduction in empiric extended-spectrum days of therapy (rate ratio, 0.72 [95% CI, 0.66-0.78]; P < .001). Safety outcomes of mean days to ICU transfer (6.5 vs 7.1 days) and hospital length of stay (6.8 vs 7.1 days) did not differ significantly between the routine and CPOE intervention groups. Conclusions and Relevance: Empiric extended-spectrum antibiotic use was significantly lower among adults admitted with pneumonia to non-ICU settings in hospitals using education, feedback, and CPOE prompts recommending standard-spectrum antibiotics for patients at low risk of MDRO infection, compared with routine stewardship practices. Hospital length of stay and days to ICU transfer were unchanged. Trial Registration: ClinicalTrials.gov Identifier: NCT03697070.

Association of Early Opioid Withdrawal Treatment Strategy and Patient-Directed Discharge Among Hospitalized Patients with Opioid Use Disorder.

BACKGROUND: Medical hospitalizations for people with opioid use disorder (OUD) frequently result in patient-directed discharges (PDD), often due to untreated pain and withdrawal. OBJECTIVE: To investigate the association between early opioid withdrawal management strategies and PDD. DESIGN: Retrospective cohort study using three datasets representing 362 US hospitals. PARTICIPANTS: Adult patients hospitalized between 2009 and 2015 with OUD (as identified using ICD-9-CM codes or inpatient buprenorphine administration) and no PDD on the day of admission. INTERVENTIONS: Opioid withdrawal management strategies were classified based on day-of-admission receipt of any of the following treatments: (1) medications for OUD (MOUD) including methadone or buprenorphine, (2) other opioid analgesics, (3) adjunctive symptomatic medications without opioids (e.g., clonidine), and (4) no withdrawal treatment. MAIN MEASURES: PDD was assessed as the main outcome and hospital length of stay as a secondary outcome. KEY RESULTS: Of 6,715,286 hospitalizations, 127,158 (1.9%) patients had OUD and no PDD on the day of admission, of whom 7166 (5.6%) had a later PDD and 91,051 (71.6%) patients received some early opioid withdrawal treatment (22.3% MOUD; 43.4% opioid analgesics; 5.9% adjunctive medications). Compared to no withdrawal treatment, MOUD was associated with a lower risk of PDD (adjusted odds ratio [aOR] = 0.73, 95%CI 0.68-0.8, p < .001), adjunctive treatment alone was associated with higher risk (aOR = 1.13, 95%CI: 1.01-1.26, p = .031), and treatment with opioid analgesics alone was associated with similar risk (aOR 0.95, 95%CI: 0.89-1.02, p = .148). Among those with PDD, both MOUD (adjusted incidence rate ratio [aIRR] = 1.24, 95%CI: 1.17-1.3, p < .001) and opioid analgesic treatments (aIRR = 1.39, 95%CI: 1.34-1.45, p < .001) were associated with longer hospital stays. CONCLUSIONS: MOUD was associated with decreased risk of PDD but was utilized in < 1 in 4 patients. Efforts are needed to ensure all patients with OUD have access to effective opioid withdrawal management to improve the likelihood they receive recommended hospital care.

Nasal Iodophor Antiseptic vs Nasal Mupirocin Antibiotic in the Setting of Chlorhexidine Bathing to Prevent Infections in Adult ICUs: A Randomized Clinical Trial.

Importance: Universal nasal mupirocin plus chlorhexidine gluconate (CHG) bathing in intensive care units (ICUs) prevents methicillin-resistant Staphylococcus aureus (MRSA) infections and all-cause bloodstream infections. Antibiotic resistance to mupirocin has raised questions about whether an antiseptic could be advantageous for ICU decolonization. Objective: To compare the effectiveness of iodophor vs mupirocin for universal ICU nasal decolonization in combination with CHG bathing. Design, Setting, and Participants: Two-group noninferiority, pragmatic, cluster-randomized trial conducted in US community hospitals, all of which used mupirocin-CHG for universal decolonization in ICUs at baseline. Adult ICU patients in 137 randomized hospitals during baseline (May 1, 2015-April 30, 2017) and intervention (November 1, 2017-April 30, 2019) were included. Intervention: Universal decolonization involving switching to iodophor-CHG (intervention) or continuing mupirocin-CHG (baseline). Main Outcomes and Measures: ICU-attributable S aureus clinical cultures (primary outcome), MRSA clinical cultures, and all-cause bloodstream infections were evaluated using proportional hazard models to assess differences from baseline to intervention periods between the strategies. Results were also compared with a 2009-2011 trial of mupirocin-CHG vs no decolonization in the same hospital network. The prespecified noninferiority margin for the primary outcome was 10%. Results: Among the 801 668 admissions in 233 ICUs, the participants' mean (SD) age was 63.4 (17.2) years, 46.3% were female, and the mean (SD) ICU length of stay was 4.8 (4.7) days. Hazard ratios (HRs) for S aureus clinical isolates in the intervention vs baseline periods were 1.17 for iodophor-CHG (raw rate: 5.0 vs 4.3/1000 ICU-attributable days) and 0.99 for mupirocin-CHG (raw rate: 4.1 vs 4.0/1000 ICU-attributable days) (HR difference in differences significantly lower by 18.4% [95% CI, 10.7%-26.6%] for mupirocin-CHG, P < .001). For MRSA clinical cultures, HRs were 1.13 for iodophor-CHG (raw rate: 2.3 vs 2.1/1000 ICU-attributable days) and 0.99 for mupirocin-CHG (raw rate: 2.0 vs 2.0/1000 ICU-attributable days) (HR difference in differences significantly lower by 14.1% [95% CI, 3.7%-25.5%] for mupirocin-CHG, P = .007). For all-pathogen bloodstream infections, HRs were 1.00 (2.7 vs 2.7/1000) for iodophor-CHG and 1.01 (2.6 vs 2.6/1000) for mupirocin-CHG (nonsignificant HR difference in differences, -0.9% [95% CI, -9.0% to 8.0%]; P = .84). Compared with the 2009-2011 trial, the 30-day relative reduction in hazards in the mupirocin-CHG group relative to no decolonization (2009-2011 trial) were as follows: S aureus clinical cultures (current trial: 48.1% [95% CI, 35.6%-60.1%]; 2009-2011 trial: 58.8% [95% CI, 47.5%-70.7%]) and bloodstream infection rates (current trial: 70.4% [95% CI, 62.9%-77.8%]; 2009-2011 trial: 60.1% [95% CI, 49.1%-70.7%]). Conclusions and Relevance: Nasal iodophor antiseptic did not meet criteria to be considered noninferior to nasal mupirocin antibiotic for the outcome of S aureus clinical cultures in adult ICU patients in the context of daily CHG bathing. In addition, the results were consistent with nasal iodophor being inferior to nasal mupirocin. Trial Registration: ClinicalTrials.gov Identifier: NCT03140423.

The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections.

BACKGROUND: The profound changes wrought by coronavirus disease 2019 (COVID-19) on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. METHODS: In 148 HCA Healthcare-affiliated hospitals, from 1 March 2020 to 30 September 2020, and a subset of hospitals with microbiology and cluster data through 31 December 2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month. RESULTS: Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% confidence interval [CI]: 23-108%) more CLABSI, 43% (95% CI: 8-90%) more CAUTI, and 44% (95% CI: 10-88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus, and Gram-negative organisms, were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. CONCLUSIONS: COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

The Limited Utility of Ranking Hospitals Based on Their Colon Surgery Infection Rates.

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) use colon surgical site infection (SSI) rates to rank hospitals and apply financial penalties. The CMS' risk-adjustment model omits potentially impactful variables that might disadvantage hospitals with complex surgical populations. METHODS: We analyzed adult patients who underwent colon surgery within facilities associated with HCA Healthcare from 2014 to 2016. SSIs were identified from National Health Safety Network (NHSN) reporting. We trained and validated 3 SSI prediction models, using (1) current CMS model variables, including hospital-specific random effects (HCA-adapted CMS model); (2) demographics and claims-based comorbidities (expanded-claims model); and (3) demographics, claims-based comorbidities, and NHSN variables (claims-plus-electronic health record [EHR] model). Discrimination, calibration, and resulting rankings were compared among all models and the current CMS model with published coefficient values. RESULTS: We identified 39 468 colon surgeries in 149 hospitals, resulting in 1216 (3.1%) SSIs. Compared to the HCA-adapted CMS model, the expanded-claims model had similar performance (c-statistic, 0.65 vs 0.67, respectively), while the claims-plus-EHR model was more accurate (c-statistic, 0.70; 95% confidence interval, .67-.73; P = .004). The sampling variation, due to the low surgical volume and small number of infections, contributed 74% of the total variation in observed SSI rates between hospitals. When CMS model rankings were compared to those from the expanded-claims and claims-plus-EHR models, 18 (15%) and 26 (22%) hospitals changed quartiles, respectively, and 10 (8.3%) and 12 (10%) hospitals changed into or out of the lowest-performing quartile, respectively. CONCLUSIONS: An expanded set of variables improved colon SSI risk predictions and quartile assignments, but low procedure volumes and SSI events remain a barrier to effectively comparing hospitals.

Medical chart validation of inpatient diagnosis codes for transfusion-related acute lung injury 2013-2015.

INTRODUCTION: Transfusion-related acute lung injury (TRALI), an adverse event occurring during or within 6 hours of transfusion, is a leading cause of transfusion-associated fatalities reported to the US Food and Drug Administration. There is limited information on the validity of diagnosis codes for TRALI recorded in inpatient electronic medical records (EMRs). STUDY DESIGNS AND METHODS: We conducted a validation study to establish the positive predictive value (PPV) of TRALI International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes recorded within a large hospital system between 2013 and 2015. A physician with critical care expertise confirmed the TRALI diagnosis. As TRALI is likely underdiagnosed, we used the specific code (518.7), and codes for respiratory failure (518.82) in combination with transfusion reaction (999.80, 999.89, E934.7). RESULTS: Among almost four million inpatient stays, we identified 208 potential TRALI cases with ICD-9-CM codes and reviewed 195 medical records; 68 (35%) met clinical definitions for TRALI (26 [38%] definitive, 15 [22%] possible, 27 [40%] delayed). Overall, the PPV for all inpatient TRALI diagnoses was 35% (95% confidence interval (CI), 28-42). The PPV for the TRALI-specific code was 44% (95% CI, 35-54). CONCLUSION: We observed low PPVs (<50%) for TRALI ICD-9-CM diagnosis codes as validated by medical charts, which may relate to inconsistent code use, incomplete medical records, or other factors. Future studies using TRALI diagnosis codes in EMR databases may consider confirming diagnoses with medical records, assessing TRALI ICD, Tenth Revision, Clinical Modification codes, or exploring alternative ways for of accurately identifying TRALI in EMR databases. KEY POINTS: In 169 hospitals, we identified 208 potential TRALI cases, reviewed 195 charts, and confirmed 68 (35%) cases met TRALI clinical definitions. As many potential TRALI cases identified with diagnosis codes did not meet clinical definitions, medical record confirmation may be prudent.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System.

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post-market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID-19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID-19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi-pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID-19, validated a diagnosis-code based algorithm for identifying patients with COVID-19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID-19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID-19 prevention and treatment.

Risk factors for suicide attempt in children, adolescents, and young adults hospitalized for mental health disorders.

BACKGROUND: Suicide is a leading cause of death among children, adolescents, and young adults (AYA), and mental health disorders are a major contributing factor. Yet, suicidal behaviors among children and AYA with mental health concerns remain understudied and age-specific risk factors are poorly understood. We examined the risk factors for suicide attempt in children and AYA with mental health disorders across three age groups: pre-adolescent children (aged ≤ 12), adolescents (aged 13-17), and young adults (aged 18-25). METHODS: A cross-sectional study of children and AYA hospitalized for a mental health disorder (n = 18,018) at a private hospital system with 141 facilities across the United States (year 2014). RESULTS: Suicide attempts six months prior to hospitalization were reported in 12.1% (n = 177) pre-adolescent children, 22% (n = 1476) adolescents, and 17.9% (n = 1766) young adults. Evidence of psychological trauma was present in 55.4% of pre-adolescent children, 51.2% of adolescents, and 44.5% of young adults. Predictors for suicide attempt observed across all three age groups included the following: female sex, depressive disorder, and being a victim of bullying. Risk factors for suicide attempt specific to pre-adolescent children included being uninsured and having an unsafe home or school environment. Among AYA, suicide attempt was associated with non-Hispanic white, family history of suicide, emotional traumas, and other traumatic experiences. Alcohol use disorder was also a significant predictor of suicide attempt in young adults. CONCLUSIONS: Suicide attempts among children and AYA admitted to a hospital with mental health concerns are highly prevalent. Socioeconomic stressors appeared to be an important contributing factor of suicidal behavior in pre-adolescent children but not in older AYA. Effective suicide prevention strategies targeting children and AYA would need to consider age-specific risk factors.

Variation in Identifying Sepsis and Organ Dysfunction Using Administrative Versus Electronic Clinical Data and Impact on Hospital Outcome Comparisons.

OBJECTIVES: Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data. CONCLUSIONS: Variation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.

Evaluating the Risk Factors for Hospital-Onset Clostridium difficile Infections in a Large Healthcare System.

In this multicenter retrospective cohort study of over 1 million patients at 150 US hospitals, proton pump inhibitors increased the odds of a patient having hospital-onset Clostridium difficile infection as did third and fourth generation cephalosporins, carbapenems, and piperacillin/tazobactam. These findings support appropriate prescribing of acid-suppression therapy and high-risk antibiotics.

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown. METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods. RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92). CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

Treatment and care received by children hospitalized with COVID-19 in a large hospital network in the United States, February 2020 to September 2021.

We described care received by hospitalized children with COVID-19 or multi-system inflammatory syndrome (MIS-C) prior to the 2021 COVID-19 Omicron variant surge in the US. We identified hospitalized children <18 years of age with a COVID-19 or MIS-C diagnosis (COVID-19 not required), separately, from February 2020-September 2021 (n = 126 hospitals). We described high-risk conditions, inpatient treatments, and complications among these groups. Among 383,083 pediatric hospitalizations, 2,186 had COVID-19 and 395 had MIS-C diagnosis. Less than 1% had both COVID-19 and MIS-C diagnosis (n = 154). Over half were >6 years old (54% COVID-19, 70% MIS-C). High-risk conditions included asthma (14% COVID-19, 11% MIS-C), and obesity (9% COVID-19, 10% MIS-C). Pulmonary complications in children with COVID-19 included viral pneumonia (24%) and acute respiratory failure (11%). In reference to children with COVID-19, those with MIS-C had more hematological disorders (62% vs 34%), sepsis (16% vs 6%), pericarditis (13% vs 2%), myocarditis (8% vs 1%). Few were ventilated or died, but some required oxygen support (38% COVID-19, 45% MIS-C) or intensive care (42% COVID-19, 69% MIS-C). Treatments included: methylprednisolone (34% COVID-19, 75% MIS-C), dexamethasone (25% COVID-19, 15% MIS-C), remdesivir (13% COVID-19, 5% MIS-C). Antibiotics (50% COVID-19, 68% MIS-C) and low-molecular weight heparin (17% COVID-19, 34% MIS-C) were frequently administered. Markers of illness severity among hospitalized children with COVID-19 prior to the 2021 Omicron surge are consistent with previous studies. We report important trends on treatments in hospitalized children with COVID-19 to improve the understanding of real-world treatment patterns in this population.

The impact of surgical volume on hospital ranking using the standardized infection ratio.

The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014-2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability.

Incidence and Outcomes of Non-Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria.

Importance: Non-ventilator-associated hospital-acquired pneumonia (NV-HAP) is a common and deadly hospital-acquired infection. However, inconsistent surveillance methods and unclear estimates of attributable mortality challenge prevention. Objective: To estimate the incidence, variability, outcomes, and population attributable mortality of NV-HAP. Design, Setting, and Participants: This cohort study retrospectively applied clinical surveillance criteria for NV-HAP to electronic health record data from 284 US hospitals. Adult patients admitted to the Veterans Health Administration hospital from 2015 to 2020 and HCA Healthcare hospitals from 2018 to 2020 were included. The medical records of 250 patients who met the surveillance criteria were reviewed for accuracy. Exposures: NV-HAP, defined as sustained deterioration in oxygenation for 2 or more days in a patient who was not ventilated concurrent with abnormal temperature or white blood cell count, performance of chest imaging, and 3 or more days of new antibiotics. Main Outcomes and Measures: NV-HAP incidence, length-of-stay, and crude inpatient mortality. Attributable inpatient mortality by 60 days follow-up was estimated using inverse probability weighting, accounting for both baseline and time-varying confounding. Results: Among 6 022 185 hospitalizations (median [IQR] age, 66 [54-75] years; 1 829 475 [26.1%] female), there were 32 797 NV-HAP events (0.55 per 100 admissions [95% CI, 0.54-0.55] per 100 admissions and 0.96 per 1000 patient-days [95% CI, 0.95-0.97] per 1000 patient-days). Patients with NV-HAP had multiple comorbidities (median [IQR], 6 [4-7]), including congestive heart failure (9680 [29.5%]), neurologic conditions (8255 [25.2%]), chronic lung disease (6439 [19.6%]), and cancer (5,467 [16.7%]); 24 568 cases (74.9%) occurred outside intensive care units. Crude inpatient mortality was 22.4% (7361 of 32 797) for NV-HAP vs 1.9% (115 530 of 6 022 185) for all hospitalizations; 12 449 (8.0%) were discharged to hospice. Median [IQR] length-of-stay was 16 (11-26) days vs 4 (3-6) days. On medical record review, pneumonia was confirmed by reviewers or bedside clinicians in 202 of 250 patients (81%). It was estimated that NV-HAP accounted for 7.3% (95% CI, 7.1%-7.5%) of all hospital deaths (total hospital population inpatient death risk of 1.87% with NV-HAP events included vs 1.73% with NV-HAP events excluded; risk ratio, 0.927; 95% CI, 0.925-0.929). Conclusions and Relevance: In this cohort study, NV-HAP, which was defined using electronic surveillance criteria, was present in approximately 1 in 200 hospitalizations, of whom 1 in 5 died in the hospital. NV-HAP may account for up to 7% of all hospital deaths. These findings underscore the need to systematically monitor NV-HAP, define best practices for prevention, and track their impact.

Omega-3 fatty acid augmentation of citalopram treatment for patients with major depressive disorder.

OBJECTIVE: The objective of this study was to explore the efficacy of combination therapy with citalopram plus omega-3 fatty acids versus citalopram plus placebo (olive oil) in the initial treatment of individuals with major depressive disorder (MDD). We hypothesized that combination therapy would lead not only to greater efficacy but also to a more rapid onset of therapeutic response. METHODS: Forty-two subjects participated in this 9-week randomized, masked, placebo-controlled study of combination therapy (two 1 g capsules containing a blend of 900 mg of eicosapentaenoic acid, 200 mg of and docosahexaenoic acid, and 100 mg of other omega-3 fatty acids twice daily plus citalopram) versus monotherapy (two 1 g capsules of olive oil per day plus citalopram) treatment of MDD. RESULTS: The combination therapy demonstrated significantly greater improvement in Hamilton Depression Rating scale scores over time (F = 7.32; df 1,177; P = 0.008) beginning at week 4 (t = -2.48; df 177; P = 0.014). CONCLUSIONS: Combination therapy was more effective than monotherapy in decreasing signs and symptoms of MDD during the 8 weeks of active treatment; however, combination therapy did not seem to enhance the speed of the initial antidepressant response. These findings suggest that there may be an advantage to combining omega-3 fatty acids with a selective serotonin uptake inhibitor in the initial treatment of individuals with MDD. A larger definitive study is warranted.

Discontinuation of atorvastatin use in hospital is associated with increased risk of mortality in COVID-19 patients.

BACKGROUND: Statins are a commonly used class of drugs, and reports have suggested that their use may affect COVID-19 disease severity and mortality risk. OBJECTIVE: The purpose of this analysis was to determine the effect of discontinuation of previous atorvastatin therapy in patients hospitalized for COVID-19 on the risk of mortality and ventilation. METHODS: Data from 146,413 hospitalized COVID-19 patients were classified according to statin therapy. Home + in hospital atorvastatin use (continuation of therapy); home + no in hospital atorvastatin use (discontinuation of therapy); no home + no in hospital atorvastatin use (no statins). Logistic regression was performed to assess the association between atorvastatin administration and either mortality or use of mechanical ventilation during the encounter. RESULTS: Continuous use of atorvastatin (home and in hospital) was associated with a 35% reduction in the odds of mortality compared to patients who received atorvastatin at home but not in hospital (odds ratio [OR]: 0.65, 95% confidence interval [CI]: 0.59-0.72, p < .001). Similarly, the odds of ventilation were lower with continuous atorvastatin therapy (OR: 0.70, 95% CI: 0.64-0.77, p < .001). CONCLUSIONS: Discontinuation of previous atorvastatin therapy is associated with worse outcomes for COVID-19 patients. Providers should consider maintaining existing statin therapy for patients with known or suspected previous use.

Using inpatient electronic medical records to study influenza for pandemic preparedness.

BACKGROUND: We assessed the ability to identify key data relevant to influenza and other respiratory virus surveillance in a large-scale US-based hospital electronic medical record (EMR) dataset using seasonal influenza as a use case. We describe characteristics and outcomes of hospitalized influenza cases across three seasons. METHODS: We identified patients with an influenza diagnosis between March 2017 and March 2020 in 140 US hospitals as part of the US FDA's Sentinel System. We calculated descriptive statistics on the presence of high-risk conditions, influenza antiviral administrations, and severity endpoints. RESULTS: Among 5.1 million hospitalizations, we identified 29,520 hospitalizations with an influenza diagnosis; 64% were treated with an influenza antiviral within 2 days of admission, and 25% were treated >2 days after admission. Patients treated >2 days after admission had more comorbidities than patients treated within 2 days of admission. Patients never treated during hospitalization had more documentation of cardiovascular and other diseases than treated patients. We observed more severe endpoints in patients never treated (death = 3%, mechanical ventilation [MV] = 9%, intensive care unit [ICU] = 26%) or patients treated >2 days after admission (death = 2%, MV = 14%, ICU = 32%) than in patients treated earlier (treated on admission: death = 1%, MV = 5%, ICU = 23%, treated within 2 days of admission: death = 1%, MV = 7%, ICU = 27%). CONCLUSIONS: We identified important trends in influenza severity related to treatment timing in a large inpatient dataset, laying the groundwork for the use of this and other inpatient EMR data for influenza and other respiratory virus surveillance.