Quality Improvement in Infection COntrol and Sepsis Management in MOdel Regions

Quality Improvement in Infection COntrol and Sepsis Management in MOdel Regions / Deutsches Qualitaetsbuendnis Sepsis (German Quality Network Sepsis)

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT02820675

Acronym

icosmos

Enrollment

Registered

2016-07-01

Start date

2015-08-31

Completion date

2019-07-31

Last updated

2020-07-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Sepsis, Septic Shock, Severe Sepsis

Keywords

quality improvement, routine data, risk adjustment, sepsis, severe sepsis, septic shock, quality of coding

Brief summary

The German Quality Network Sepsis is an association trying to improve quality of care for patients with sepsis, severe sepsis and septic shock or being in risc thereof. The icosmos trial investigates the impact on the use of routine data, a risk adjustment algorithm and feedback to all hospitals as well as a structured implementation for regular screening for deteriorating patients, and education on in-hospital mortality.

Detailed description

All German hospitals provide a set of routine data to federal adminsitration for reimbursement. This dataset includes all ICD-10-Codes (German adaption) as well as all procedure codes. This dataset is pseudonomized and risk adjusted for every individual case and afterwards feedbacked to each participating hospital including all accumulated data from all other hospitals as well as the German Average for sepsis mortalities provided by the German Federal Institute of Statistcs for benchmarkings. Based on this data, the icosmos trial will provide support to all participating hospitals for two major strategies to be implemented 1. An algorithm for structured analysis of 1. low-risk-fatalities as potentially preventable deaths for underlying problems in diagnosis, treatment, etc 2. high-risk-survivors as best practice 3. coding errors for improvement of coding quality 2. a structured sreening program of every patient on every ward in every shift for deterioration and support algorithms including consiliary support and outreach teams as well as structured education The icosmos trial will investigate the effects as well on quality of coding and sepsis incidence as well as on sepsis-related sepsis in-hospital mortality for participating hospitals in comparison to the German Average.

Interventions

OTHERstructured case analysis

implementation of a structured analysis of cases coded as sepsis, severe sepsis, or septic shock after risk adjustment for low-risk-fatalities, high-risk-survivors, and coding errors for preventable deaths, best practice, quality improvement and improvement in coding precision.

OTHERstrucured screening every patient, every shift

implementation of a strutured screening and rescue algorithm on regular wards, emergency departements, and intermediate and intensive care units by checking every patient in every shifts for clinical signs of deterioration and apllying a rescue algorithm including consiliary help and/or the help of outreach teams

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Eligibility

Sex/Gender

ALL

Age

16 Years to No maximum

Healthy volunteers

Inclusion criteria

for hospitals: * hospitals willing to participate Inclusion Criteria for patients / patients' data * age over 16 * coding of sepsis, severe sepsis, septic shock

Exclusion criteria

for hospitals: * no intensive care unit on site

Design outcomes

Primary

Measure	Time frame	Description
in-hospital mortality	3 years	in-hospital mortality of all patients coded with sepsis, severe sepsis and septic shock in absolute numbers and in relation to the German average
sepsis codings	3 years	developement of coding of sepsis, severe sepsis and septic shock in absolte numbers and in relation to the German Average

Countries

Germany

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 24, 2026