scholarly journals 516: SOCIAL DETERMINANTS OF HEALTH IMPACT HOSPITAL LENGTH OF STAY FOR CHILDREN WITH SEVERE SEPSIS

2021 ◽  
Vol 50 (1) ◽  
pp. 249-249
Author(s):  
Alina West ◽  
Hunter Hamilton ◽  
Nariman Ammar ◽  
Fatma Gunturkun ◽  
Tamekia Jones ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Length Of Stay ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Health Impact ◽  
Hospital Length ◽  
Determinants Of Health ◽  
Hospital Length Of Stay

scholarly journals 770. Multimodal Sepsis Performance Improvement Initiative Improves Early and Appropriate Treatment, Reduces Sepsis-related Readmissions, and Reduces Overall Mortality

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S343-S343
Author(s):  
Seife Yohannes
Keyword(s):  
Septic Shock ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Performance Improvement ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Intervention Period ◽  
Post Intervention ◽  
Post Intervention Period ◽  
Sepsis And Septic Shock

Abstract Background CMS has implemented the SEP-1 Core Measure, which mandates that hospitals implement sepsis quality improvement initiatives. At our hospital, a 900-bed tertiary hospital, a sepsis performance improvement initiative was implemented in April 2016. In this study, we analyzed patient outcomes before and after these interventions. Methods We studied coding data in patients with a diagnosis of Sepsis reported to CMS using a third-party performance improvement database between October, 2015 and July, 2017. The interventions included a hospital-wide education campaign about sepsis; a 24–7 electronic warning system (EWS) using SIRS criteria; a rapid response nursing team that monitors the EWS; a 24–7 mid-level provider team; a database to monitor compliance and timely treatment; and education in sepsis documentation and coding. We performed a before and after analysis of patient outcomes. Results A total of 4,102 patients were diagnosed with sepsis during the study period. 861 (21%) were diagnosed during the pre-intervention period and 3,241 (80%) were diagnosed in the post-intervention period. The overall incidence of sepsis, severe sepsis, and septic shock were 59%, 13%, and 28% consecutively. Regression analysis showed age, admission through the ED, and severity of illness as independent risk factors for increased mortality. Adjusted for these risk factors, the incidence of severe sepsis and septic was reduced by 5.3% and 6.9% in the post-intervention period, while the incidence of simple sepsis increased by 12%. In the post-intervention period, compliance with all 6 CMS mandated sepsis bundle interventions improved from 11% to 37% (P = 0.01); hospital length of stay was reduced by 1.8 days (P = 0.05); length of stay above predicted was less by 1.5 days (P = 0.05); re-admission rate was reduced by 1.6% (P = 0.05); and death from any sepsis diagnosis was reduced 4.5% (P = 0.01). Based on an average of 2000 sepsis cases at our hospital, this amounted to 90 lives saved per year. Death from severe sepsis and septic shock both were also reduced by 5% (P = 0.01) and 6.5% (P = 0.01). Conclusion A multi-modal sepsis performance improvement initiative reduced the incidence of severe sepsis and septic shock, reduced hospital length of stay, reduced readmission rates, and reduced all-cause mortality. Disclosures All authors: No reported disclosures.

The influence of social determinants of health on single-level anterior cervical discectomy and fusion outcomes

2021 ◽  
pp. 1-6
Keyword(s):  
Length Of Stay ◽  
Postoperative Complication ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Revision Surgery ◽  
Determinants Of Health ◽  
Anterior Cervical Discectomy ◽  
Complication Rates ◽  
Cervical Discectomy ◽  
Single Level

OBJECTIVE Methods of reducing complications in individuals electing to undergo anterior cervical discectomy and fusion (ACDF) rely upon understanding at-risk patient populations, among other factors. This study aims to investigate the interplay between social determinants of health (SDOH) and postoperative complication rates, length of stay, revision surgery, and rates of postoperative readmission at 30 and 90 days in individuals electing to have single-level ACDF. METHODS Using MARINER30, a database that contains claims information from all payers, patients were identified who underwent single-level ACDF between 2010 and 2019. Identification of patients experiencing disparities in 1 of 6 categories of SDOH was completed using ICD-9 and ICD-10 (International Classifications of Diseases, Ninth and Tenth Revisions) codes. The population was propensity matched into 2 cohorts based on comorbidity status: those with SDOH versus those without. RESULTS A total of 10,030 patients were analyzed; there were 5015 (50.0%) in each cohort. The rates of any postoperative complication (12.0% vs 4.6%, p < 0.001); pseudarthrosis (3.4% vs 2.6%, p = 0.017); instrumentation removal (1.8% vs 1.2%, p = 0.033); length of stay (2.54 ± 5.9 days vs 2.08 ± 5.07 days, p < 0.001 [mean ± SD]); and revision surgery (9.7% vs 4.2%, p < 0.001) were higher in the SDOH group compared to patients without SDOH, respectively. Patients with any SDOH had higher odds of perioperative complications (OR 2.8, 95% CI 2.43–3.33), pseudarthrosis (OR 1.3, 95% CI 1.06–1.68), revision surgery (OR 2.4, 95% CI 2.04–2.85), and instrumentation removal (OR 1.4, 95% CI 1.04–2.00). CONCLUSIONS In patients who underwent single-level ACDF, there is an association between SDOH and higher complication rates, longer stay, increased need for instrumentation removal, and likelihood of revision surgery.

scholarly journals Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

2020 ◽  
Vol 27 (1) ◽  
pp. e100109 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

BackgroundSevere sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.ObjectiveThe purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.DesignProspective clinical outcomes evaluation.SettingEvaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.ParticipantsAnalyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay (‘sepsis-related’ patients).InterventionsMachine learning algorithm for severe sepsis prediction.Outcome measuresIn-hospital mortality, length of stay and 30-day readmission rates.ResultsHospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.ConclusionsReductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.Trial registration numberNCT03960203

scholarly journals “Person with disabilities” and their “social determinants of health”- the neglected paradigm of public health

2018 ◽  
pp. 11
Author(s):  
Mohammad Monjurul Karim
Keyword(s):  
Public Health ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Action Plan ◽  
Transverse Myelitis ◽  
Health Impact ◽  
Determinants Of Health ◽  
World Health ◽  
Preventive Program ◽  
Predisposing Factor

This study was conducted to aware the audience to be more concern about “person with disabilities” and their “social determinants of health” as an emerging area in public health. WHO mentioned, 15% of the global populations are suffering from some form of disabilities and the number is higher comparing the report of 1970s. Public health aim to prevent mortality, morbidity and disability in different sectors (Donald, Lollar & John, 2003). But its alarming that disability preventive program often neglected in public health programs. Even the link between diseases and disability is often overlooked in several cases like GBS, encephalitis, transverse myelitis etc. Reduced inflow and increased outflow of finance and social determinants of health impact negatively on the life of the person with disabilities. Continuous effort to improve the social determinants of health worked tremendously over the last few decades to improve the life of human being globally, which is unfortunately sometimes worked as predisposing factor to the increased number of disabilities. But effort to reduce burden of disability and to improve SDH is just negligible. The 67th World Health Assembly adopted a resolution endorsing the WHO global disability action plan 2014–2021: Better health for all people with disability. It reflects the major shift in global understanding and responses towards disability. It could be concluded that it’s the high time to look more precisely in this neglected area whiting various discourse of SDH which will be a big burden of the public health in coming days. More research is required to minimize number of disability as well the after math of disability.

scholarly journals Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

2018 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Retrospective Analysis ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

AbstractObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.SettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.ParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.ResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.ConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.KEY POINTSQuestionIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?FindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.MeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.STRENGTHS AND LIMITATIONS OF THIS STUDYA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.A multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.The required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.Only adults in US hospitals were included in this study.For the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.

Leveraging social determinants of health to reduce hospital length of stay: A pilot quality improvement project to address inpatient cancer patients' barriers during the COVID 19 pandemic.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 233-233
Author(s):  
Sailaja Kamaraju ◽  
Dave Atkinson ◽  
Thomas Wetzel ◽  
Tamiah Wright ◽  
John A. Charlson ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Length Of Stay ◽  
Social Determinants Of Health ◽  
Cancer Patients ◽  
Social Determinants ◽  
Solid Tumor ◽  
Determinants Of Health ◽  
Patient Specific ◽  
Discharge Process ◽  
Oncology Patients

233 Background: Prior reports from our institution demonstrated high rates of racial segregation, unfavorable social determinants of health (SDoH) in Milwaukee, WI, and statewide reports of inferior outcomes for cancer patients from minority communities. At the Medical College of Wisconsin's Cancer Center (Milwaukee, WI), during the first through last quarters of 2018-2019, cancer patients from the low socioeconomic status (SES) communities who were hospitalized to inpatient oncology units had an average length of stay (LOS) of 7.2 days compared to 5.6 days for high SES group. Under the auspices of the American Society of Clinical Oncology's Quality Training Program (QTP) initiative, we aimed to reduce the hospital LOS by 10% or less by May 2021 for inpatient oncology teams. Methods: A multidisciplinary team collaboration between the inpatient and outpatient providers was developed during this QI initiative. We examined LOS index data, payer types, and other diagnostic criteria for the oncology inpatient solid tumor service and two comparator services (bone marrow transplant, BMT; internal medicine). We generated workflow, a cause-and-effect diagram, and a Pareto diagram to determine the relevant factors associated with longer hospital LOS. Institution-wide implementation of the SDH screen project was launched to evaluate and address specific barriers to SDoH to expedite a safe discharge process during the pandemic. Results: Through one test of change (Plan-Do-Study-Act cycles 1, 2 &3), we identified the problem of extended LOS and patient-related barriers to discharge during this QI initiative. Compared to the baseline LOS, after the launch of the SDoH screen project, there was a 6.5% decrease in the inpatient average LOS for oncology patients (7.89 to 7.40days, p = 0.004),10.7% for BMT (15.96 days to14.26, p = 0.166), and 2.4% for Internal Medicine (4.61 to 4.50 to days, p = 0.131). There was a 10.0% decrease in LOS (8.07 to 7.26 days, p = < 0.001) for the three specialties combined. With collaboration from inpatient and outpatient providers, appropriate referrals were generated to address patient-specific SDH before discharge (i.e., transportation coordination, nutritional and physical therapy referrals, social worker assistance with food, and housing insecurities). Conclusions: In this pilot project, implementing SDoH screening-based-care delivery at the time of inpatient admission demonstrated a slight improvement in LOS for solid tumor oncology patients and provided timely referrals, opportunities to engage and explore the discharge facilities early on during the COVID 19 pandemic. With this preliminary data, we plan to continue to expand our efforts through a systemwide implementation of this SDoH survey both in the inpatient and outpatient settings to address cancer inequities.

scholarly journals Time to First Antimicrobial Administration After Onset of Sepsis in Critically Ill Children

2015 ◽  
Vol 20 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Nicholas M. Fusco ◽  
Kristine A. Parbuoni ◽  
Jill A. Morgan
Keyword(s):  
Intensive Care Unit ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Intensive Care ◽  
Length Of Stay ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Critically Ill Children ◽  
Sepsis And Septic Shock ◽  
Antimicrobial Administration

OBJECTIVES: Delay of antimicrobial administration in adult patients with severe sepsis and septic shock has been associated with a decrease in survival to hospital discharge. The primary objective of this investigation was to determine the time to first antimicrobial administration after the onset of sepsis in critically ill children. Secondary objectives included appropriateness of empiric antimicrobials and microbiological testing, fluid resuscitation during the first 24 hours after onset of sepsis, intensive care unit and hospital length of stay, and mortality. METHODS: Retrospective, chart review of all subjects less than or equal to 18 years of age admitted to the pediatric intensive care unit (PICU) with a diagnosis of sepsis between January 1, 2011, and December 31, 2012. RESULTS: A total of 72 subjects met the inclusion criteria during the study period. Median time to first antimicrobial administration by a nurse after the onset of sepsis was 2.7 (0.5–5.1) hours. Cultures were drawn prior to administration of antimicrobials in 91.7% of subjects and were repeated within 48 hours in 72.2% of subjects. Empiric antimicrobial regimens were appropriate in 91.7% of cases. The most common empiric antimicrobial regimens included piperacillin/tazobactam plus vancomycin in 19 subjects (26.4%) and ceftriaxone plus vancomycin in 15 subjects (20.8%). Median PICU length of stay was 129 (64.6–370.9) hours, approximately 5 days, and median hospital length of stay was 289 (162.5–597.1) hours, approximately 12 days. There were 4 deaths during the study period. CONCLUSIONS: Time to first antimicrobial administration after onset of sepsis was not optimal and exceeded the recommendations set forth in international guidelines. At our institution, the process for treating pediatric patients with severe sepsis and septic shock should be modified to increase compliance with national guidelines.

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