Association Between Hospital Type and Resilience During COVID-19 Caseload Stress : A Retrospective Cohort Study.

BACKGROUND: Imbalances between hospital caseload and care resources that strained U.S. hospitals during the pandemic have persisted after the pandemic amid ongoing staff shortages. Understanding which hospital types were more resilient to pandemic overcrowding-related excess deaths may prioritize patient safety during future crises. OBJECTIVE: To determine whether hospital type classified by capabilities and resources (that is, extracorporeal membrane oxygenation [ECMO] capability, multiplicity of intensive care unit [ICU] types, and large or small hospital) influenced COVID-19 volume-outcome relationships during Delta wave surges. DESIGN: Retrospective cohort study. SETTING: 620 U.S. hospitals in the PINC AI Healthcare Database. PARTICIPANTS: Adult inpatients with COVID-19 admitted July to November 2021. MEASUREMENTS: Hospital-months were ranked by previously validated surge index (severity-weighted COVID-19 inpatient caseload relative to hospital bed capacity) percentiles. Hierarchical models were used to evaluate the effect of log-transformed surge index on the marginally adjusted probability of in-hospital mortality or discharge to hospice. Effect modification was assessed for by 4 mutually exclusive hospital types. RESULTS: Among 620 hospitals recording 223 380 inpatients with COVID-19 during the Delta wave, there were 208 ECMO-capable, 216 multi-ICU, 36 large (≥200 beds) single-ICU, and 160 small (<200 beds) single-ICU hospitals. Overall, 50 752 (23%) patients required admission to the ICU, and 34 274 (15.3%) died. The marginally adjusted probability for mortality was 5.51% (95% CI, 4.53% to 6.50%) per unit increase in the log surge index (strain attributable mortality = 7375 [CI, 5936 to 8813] or 1 in 5 COVID-19 deaths). The test for interaction showed no difference (P = 0.32) in log surge index-mortality relationship across 4 hospital types. Results were consistent after excluding transferred patients, restricting to patients with acute respiratory failure and mechanical ventilation, and using alternative strain metrics. LIMITATION: Residual confounding. CONCLUSION: Comparably detrimental relationships between COVID-19 caseload and survival were seen across all hospital types, including highly advanced centers, and well beyond the pandemic's learning curve. These lessons from the pandemic heighten the need to minimize caseload surges and their effects across all hospital types during public health and staffing crises. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center.

Assessing Clinician Utilization of Next-Generation Antibiotics Against Resistant Gram-Negative Infections in U.S. Hospitals : A Retrospective Cohort Study.

BACKGROUND: The U.S. antibiotic market failure has threatened future innovation and supply. Understanding when and why clinicians underutilize recently approved gram-negative antibiotics might help prioritize the patient in future antibiotic development and potential market entry rewards. OBJECTIVE: To determine use patterns of recently U.S. Food and Drug Administration (FDA)-approved gram-negative antibiotics (ceftazidime-avibactam, ceftolozane-tazobactam, meropenem-vaborbactam, plazomicin, eravacycline, imipenem-relebactam-cilastatin, and cefiderocol) and identify factors associated with their preferential use (over traditional generic agents) in patients with gram-negative infections due to pathogens displaying difficult-to-treat resistance (DTR; that is, resistance to all first-line antibiotics). DESIGN: Retrospective cohort. SETTING: 619 U.S. hospitals. PARTICIPANTS: Adult inpatients. MEASUREMENTS: Quarterly percentage change in antibiotic use was calculated using weighted linear regression. Machine learning selected candidate variables, and mixed models identified factors associated with new (vs. traditional) antibiotic use in DTR infections. RESULTS: Between quarter 1 of 2016 and quarter 2 of 2021, ceftolozane-tazobactam (approved 2014) and ceftazidime-avibactam (2015) predominated new antibiotic usage whereas subsequently approved gram-negative antibiotics saw relatively sluggish uptake. Among gram-negative infection hospitalizations, 0.7% (2551 [2631 episodes] of 362 142) displayed DTR pathogens. Patients were treated exclusively using traditional agents in 1091 of 2631 DTR episodes (41.5%), including "reserve" antibiotics such as polymyxins, aminoglycosides, and tigecycline in 865 of 1091 episodes (79.3%). Patients with bacteremia and chronic diseases had greater adjusted probabilities and those with do-not-resuscitate status, acute liver failure, and Acinetobacter baumannii complex and other nonpseudomonal nonfermenter pathogens had lower adjusted probabilities of receiving newer (vs. traditional) antibiotics for DTR infections, respectively. Availability of susceptibility testing for new antibiotics increased probability of usage. LIMITATION: Residual confounding. CONCLUSION: Despite FDA approval of 7 next-generation gram-negative antibiotics between 2014 and 2019, clinicians still frequently treat resistant gram-negative infections with older, generic antibiotics with suboptimal safety-efficacy profiles. Future antibiotics with innovative mechanisms targeting untapped pathogen niches, widely available susceptibility testing, and evidence demonstrating improved outcomes in resistant infections might enhance utilization. PRIMARY FUNDING SOURCE: U.S. Food and Drug Administration; NIH Intramural Research Program.

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review.

OBJECTIVES: COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients. DATA SOURCES: PubMed, Embase, and Web of Science. STUDY SELECTION: English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients. DATA EXTRACTION: Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations. DATA SYNTHESIS: Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status. CONCLUSIONS: Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

Trends in Patient Transfers From Overall and Caseload-Strained US Hospitals During the COVID-19 Pandemic.

Importance: Transferring patients to other hospitals because of inpatient saturation or need for higher levels of care was often challenging during the early waves of the COVID-19 pandemic. Understanding how transfer patterns evolved over time and amid hospital overcrowding could inform future care delivery and load balancing efforts. Objective: To evaluate trends in outgoing transfers at overall and caseload-strained hospitals during the COVID-19 pandemic vs prepandemic times. Design, Setting, and Participants: This retrospective cohort study used data for adult patients at continuously reporting US hospitals in the PINC-AI Healthcare Database. Data analysis was performed from February to July 2023. Exposures: Pandemic wave, defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), Delta (June 20, 2021, to December 18, 2021), and Omicron (December 19, 2021, to February 28, 2022). Main Outcomes and Measures: Weekly trends in cumulative mean daily acute care transfers from all hospitals were assessed by COVID-19 status, hospital urbanicity, and census index (calculated as daily inpatient census divided by nominal bed capacity). At each hospital, the mean difference in transfer counts was calculated using pairwise comparisons of pandemic (vs prepandemic) weeks in the same census index decile and averaged across decile hospitals in each wave. For top decile (ie, high-surge) hospitals, fold changes (and 95% CI) in transfers were adjusted for hospital-level factors and seasonality. Results: At 681 hospitals (205 rural [30.1%] and 476 urban [69.9%]; 360 [52.9%] small with <200 beds and 321 [47.1%] large with ≥200 beds), the mean (SD) weekly outgoing transfers per hospital remained lower than the prepandemic mean of 12.1 (10.4) transfers per week for most of the pandemic, ranging from 8.5 (8.3) transfers per week during wave 1 to 11.9 (10.7) transfers per week during the Delta wave. Despite more COVID-19 transfers, overall transfers at study hospitals cumulatively decreased during each high national surge period. At 99 high-surge hospitals, compared with a prepandemic baseline, outgoing acute care transfers decreased in wave 1 (fold change -15.0%; 95% CI, -22.3% to -7.0%; P < .001), returned to baseline during wave 2 (2.2%; 95% CI, -4.3% to 9.2%; P = .52), and displayed a sustained increase in subsequent waves: 19.8% (95% CI, 14.3% to 25.4%; P < .001) in wave 3, 19.2% (95% CI, 13.4% to 25.4%; P < .001) in the Delta wave, and 15.4% (95% CI, 7.8% to 23.5%; P < .001) in the Omicron wave. Observed increases were predominantly limited to small urban hospitals, where transfers peaked (48.0%; 95% CI, 36.3% to 60.8%; P < .001) in wave 3, whereas large urban and small rural hospitals displayed little to no increases in transfers from baseline throughout the pandemic. Conclusions and Relevance: Throughout the COVID-19 pandemic, study hospitals reported paradoxical decreases in overall patient transfers during each high-surge period. Caseload-strained rural (vs urban) hospitals with fewer than 200 beds were unable to proportionally increase transfers. Prevailing vulnerabilities in flexing transfer capabilities for care or capacity reasons warrant urgent attention.

Impact of Surge Strain and Pandemic Progression on Prognostication by an Established COVID-19-Specific Severity Score.

IMPORTANCE: Many U.S. State crisis standards of care (CSC) guidelines incorporated Sequential Organ Failure Assessment (SOFA), a sepsis-related severity score, in pandemic triage algorithms. However, SOFA performed poorly in COVID-19. Although disease-specific scores may perform better, their prognostic utility over time and in overcrowded care settings remains unclear. OBJECTIVES: We evaluated prognostication by the modified 4C (m4C) score, a COVID-19-specific prognosticator that demonstrated good predictive capacity early in the pandemic, as a potential tool to standardize triage across time and hospital-surge environments. DESIGN: Retrospective observational cohort study. SETTING: Two hundred eighty-one U.S. hospitals in an administrative healthcare dataset. PARTICIPANTS: A total of 298,379 hospitalized adults with COVID-19 were identified from March 1, 2020, to January 31, 2022. m4C scores were calculated from admission diagnosis codes, vital signs, and laboratory values. MAIN OUTCOMES AND MEASURES: Hospital-surge index, a severity-weighted measure of COVID-19 caseload, was calculated for each hospital-month. Discrimination of in-hospital mortality by m4C and surge index-adjusted models was measured by area under the receiver operating characteristic curves (AUC). Calibration was assessed by training models on early pandemic waves and measuring fit (deviation from bisector) in subsequent waves. RESULTS: From March 2020 to January 2022, 298,379 adults with COVID-19 were admitted across 281 U.S. hospitals. m4C adequately discriminated mortality in wave 1 (AUC 0.779 [95% CI, 0.769-0.789]); discrimination was lower in subsequent waves (wave 2: 0.772 [95% CI, 0.765-0.779]; wave 3: 0.746 [95% CI, 0.743-0.750]; delta: 0.707 [95% CI, 0.702-0.712]; omicron: 0.729 [95% CI, 0.721-0.738]). m4C demonstrated reduced calibration in contemporaneous waves that persisted despite periodic recalibration. Performance characteristics were similar with and without adjustment for surge. CONCLUSIONS AND RELEVANCE: Mortality prediction by the m4C score remained robust to surge strain, making it attractive for when triage is most needed. However, score performance has deteriorated in recent waves. CSC guidelines relying on defined prognosticators, especially for dynamic disease processes like COVID-19, warrant frequent reappraisal to ensure appropriate resource allocation.

Epidemiology of ICU-Onset Bloodstream Infection: Prevalence, Pathogens, and Risk Factors Among 150,948 ICU Patients at 85 U.S. Hospitals.

OBJECTIVES: Bloodstream infections (BSIs) acquired in the ICU represent a detrimental yet potentially preventable condition. We determined the prevalence of BSI acquired in the ICU (ICU-onset BSI), pathogen profile, and associated risk factors. DESIGN: Retrospective cohort study. DATA SOURCES: Eighty-five U.S. hospitals in the Cerner Healthfacts Database. PATIENT SELECTION: Adult hospitalizations between January 2009 and December 2015 including a (≥ 3 d) ICU stay. DATA EXTRACTION AND DATA SYNTHESIS: Prevalence of ICU-onset BSI (between ICU Day 3 and ICU discharge) and associated pathogen and antibiotic resistance distributions were compared with BSI present on (ICU) admission (ICU-BSI POA ); and BSI present on ICU admission day or Day 2. Cox models identified risk factors for ICU-onset BSI among host, care setting, and treatment-related factors. Among 150,948 ICU patients, 5,600 (3.7%) had ICU-BSI POA and 1,306 (0.9%) had ICU-onset BSI. Of those with ICU-BSI POA , 4,359 (77.8%) were admitted to ICU at hospital admission day. Patients with ICU-onset BSI (vs ICU-BSI POA ) displayed higher crude mortality of 37.9% (vs 20.4%) ( p < 0.001) and longer median (interquartile range) length of stay of 13 days (8-23 d) (vs 5 d [3-8 d]) ( p < 0.001) (considering all ICU stay). Compared with ICU-BSI POA , ICU-onset BSI displayed more Pseudomonas , Acinetobacter , Enterococcus, Candida , and Coagulase-negative Staphylococcus species, and more methicillin-resistant staphylococci, vancomycin-resistant enterococci, ceftriaxone-resistant Enterobacter , and carbapenem-resistant Enterobacterales and Acinetobacter species, respectively. Being younger, male, Black, Hispanic, having greater comorbidity burden, sepsis, trauma, acute pulmonary or gastrointestinal presentations, and pre-ICU exposure to antibacterial and antifungal agents was associated with greater ICU-onset BSI risk after adjusted analysis. Mixed ICUs (vs medical or surgical ICUs) and urban and small/medium rural hospitals were also associated with greater ICU-onset BSI risk. The associated risk of acquiring ICU-onset BSI manifested with any duration of mechanical ventilation and 7 days after insertion of central venous or arterial catheters. CONCLUSIONS: ICU-onset BSI is a serious condition that displays a unique pathogen and resistance profile compared with ICU-BSI POA . Further scrutiny of modifiable risk factors for ICU-onset BSI may inform control strategies.

Risk Factors for Severe COVID-19 Outcomes Among Persons Aged ≥18 Years Who Completed a Primary COVID-19 Vaccination Series - 465 Health Care Facilities, United States, December 2020-October 2021.

Vaccination against SARS-CoV-2, the virus that causes COVID-19, is highly effective at preventing COVID-19-associated hospitalization and death; however, some vaccinated persons might develop COVID-19 with severe outcomes† (1,2). Using data from 465 facilities in a large U.S. health care database, this study assessed the frequency of and risk factors for developing a severe COVID-19 outcome after completing a primary COVID-19 vaccination series (primary vaccination), defined as receipt of 2 doses of an mRNA vaccine (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) or a single dose of JNJ-78436735 [Janssen (Johnson & Johnson)] ≥14 days before illness onset. Severe COVID-19 outcomes were defined as hospitalization with a diagnosis of acute respiratory failure, need for noninvasive ventilation (NIV), admission to an intensive care unit (ICU) including all persons requiring invasive mechanical ventilation, or death (including discharge to hospice). Among 1,228,664 persons who completed primary vaccination during December 2020-October 2021, a total of 2,246 (18.0 per 10,000 vaccinated persons) developed COVID-19 and 189 (1.5 per 10,000) had a severe outcome, including 36 who died (0.3 deaths per 10,000). Risk for severe outcomes was higher among persons who were aged ≥65 years, were immunosuppressed, or had at least one of six other underlying conditions. All persons with severe outcomes had at least one of these risk factors, and 77.8% of those who died had four or more risk factors. Severe COVID-19 outcomes after primary vaccination are rare; however, vaccinated persons who are aged ≥65 years, are immunosuppressed, or have other underlying conditions might be at increased risk. These persons should receive targeted interventions including chronic disease management, precautions to reduce exposure, additional primary and booster vaccine doses, and effective pharmaceutical therapy as indicated to reduce risk for severe COVID-19 outcomes. Increasing COVID-19 vaccination coverage is a public health priority.