Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries

Background Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. Objective This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. Methods An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. Results We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. Conclusions The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.


Introduction
Antimicrobial resistance (AMR) is a global health crisis [1].The report by Lord Jim O'Neill estimated that 700,000 global deaths could be attributable to AMR in 2015, and projected that the annual death toll could reach 10 million by 2050 [1].However, data of AMR surveillance from low and middle−income countries (LMICs) are scarce [1,2], and data of mortality associated with AMR infections are rarely available.A recent study estimated that 19,000 deaths are attributable to AMR infections in Thailand annually, using routinely available microbiological and hospital databases [3].The study also proposed that hospitals in LMICs should utilize routinely available microbiological and hospital admission databases to generate reports on AMR surveillance systematically [3].
Reports on AMR surveillance can have a wide range of benefits [2]; including − characterization of the frequency of resistance and organisms in different facilities and regions; − prospective and retrospective information on emerging public health threats; − evaluation and optimization of local and national standard treatment guidelines; − evaluation of the impact of interventions beyond antimicrobial guidelines that aim to reduce AMR; and − data sharing with national and international organizations to support decisions on resource allocation for interventions against AMR and to inform the implementation of action plans at national and global levels.
When reporting AMR surveillance results, it is generally recommended that (a) duplicate results of bacterial isolates are removed, and (b) reports are stratified by infection origin (community−origin or hospital−origin), if possible [2].Many hospitals in LMICs lack time and resources needed to analyze the data (particularly to deduplicate data and to generate tables and figures), write the reports, and to release the data or reports [4].
AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open−access and easy−to−use application that allows a hospital to perform data analysis independently and generate isolate−based and sample−based surveillance reports stratified by infection origin from routinely collected electronic databases.The application was built in R, which is a free software environment.The application has been placed within a user−friendly interface that only requires the user to double−click on the application icon.The AMASS application can be downloaded at: http://www.amass.websitePlease note that the AMASS application and the automatically−generated report have limitations, and require readers to understand those limitations and review the reports and summary data carefully.We encourage the user of the AMASS application to perform manual validation (such as printing and listing isolates of the species to cross check with the reports), as recommended by Clinical and Laboratory Standards Insitute (CLSI) [5] and European Antimicrobial Resistance Surveillance Network (EUCAST) [6,7].Moreover, it is important to note that the AMASS is an add−on automatized report generating tool and does not replace WHONET, Laboratory Information System (LIS), quality assurance programme, or antimicrobial surveillance systems (including the WHO GLASS).

Introduction
An overview of the data detected by the AMASS application is generated by default.The summary is based on the raw data files saved within the same folder as the application file (AMASS.bat).
Please review and validate this section carefully before proceeds to the next section.

Results
The microbiology_data file (stored in the same folder as the application file) had: 622 specimen data records with collection dates ranging from 01 Jan 1995 to 31 Jan 1995 The hospital_admission_data file (stored in the same folder as the application file) had:

NA
to NA Notes: [1] If the periods of the data in microbiology_data and hospital_admission_data files are not similar, the automatically−generated report should be interpreted with caution.The AMASS generates the reports based on the available data.

Reporting period by months:
Data was stratified by month to assist detection of missing data, and verification of whether the month distribution of data records in microbiology_data file and hospital_ admission_data file reflected the microbiology culture frequency and admission rate of the hospital, respectively.For example if the number of specimens in the microbiology_data file reported below is lower than what is expected, please check the raw data file and data dictionary files.The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period as described in the method.The number of patients with positive samples is as follows:

Total:
28 26 *The negative culture included data values specified as 'no growth' in the dictionary_for_ microbiology_data file (details on data dictionary files are in the method section) to represent specimens with negative culture for any microorganism.**Only the first isolate for each patient per specimen type, per pathogen, and per evaluation period was included in the analysis.
The following figures and tables show the proportion of patients with blood culture positive for antimicrobial non−susceptible isolates.Note: [1] Please ensure that the file names of microbiology data file (microbiology_data) and the hospital admission data file (hospital_admission_data) are identical to what is written here.Please make sure that all are lower−cases with an underscore '_' at each space.
[2] Please ensure that both microbiology and hospital admission data files have no empty rows before the row of the variable names (i.e. the variable names are the first row in both files).
[3] For the first run, an user may need to fill the data dictionary files to make sure that the AMASS application understands your variable names and values.
AMASS uses a tier−based approach.In cases when only the microbiology data file with the results of culture positive samples is available, only section one and two would be generated for users.Section three would be generated only when data on admission date are available.This is because these data are required for the stratification by origin of infection.Section four would be generated only when data of specimens with culture negative (no microbial growth) are available in the microbiology data.This is because these data are required for the sample−based approach.Section five would be generated only when both data of specimens with culture negative and admission date are available.Section six would be generated only when mortality data are available.
Mortality was calculated from the number of in−hospital deaths (numerator) over the total number of patients with blood culture positive for the organism (denominator).Please note that this is the all−cause mortality calculated using the outcome data in the data file, and may not necessarily represent the mortality directly due to the infections.

How to use data dictionary files
In cases when variable names in the microbiology and hospital admission data files were not the same as the one that AMASS used, the data dictionary files could be edited.The raw microbiology and hospital admission data files were to be left unchanged.The data dictionary files provided could be edited and re−used automatically when the microbiology and hospital admission data files were updated and the AMASS.batwere to be double−clicked again (i.e. the data dictionary files would allow the user to re−analyze data files without the need to adjust variable names and data value again every time).

Definitions:
The definitions of infection origin proposed by the WHO GLASS was used [1].In brief, community−origin bloodstream infection (BSI) was defined for patients in the hospital within the first two calendar days of admission when the first blood culture positive specimens were taken.Hospital−origin BSI was defined for patients in the hospital longer than the first two calendar days of admission when the first blood culture positive specimens were taken.In cases when the user had additional data on infection origin defined by infection control team or based on referral data, the user could edit the data dictionary file (variable name 'infection_origin') and the AMASS application would use the data of that variable to stratify the data by origin of infection instead of the above definition.However, in cases when data on infection origin were not available (as in many hospitals in LMICs), the above definition would be calculated based on admission date and specimen collection date (with cutoff of 2 calendar days) and used to classify infections as community−origin or hospital−origin.

De−duplication:
When more than one blood culture was collected during patient management, duplicated findings of the same patient were excluded (de−duplicated).Only one result was reported for each patient per sample type (blood) and surveyed organisms (listed above).For example, if two blood cultures from the same patient had E. coli , only the first would be included in the report.If there was growth of E. coli in one blood culture and of K. pneumoniae in the other blood culture, then both results would be reported.One would be for the report on E. coli and the other one would be for the report on K. pneumoniae.

Investigator team
The

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Proportion of non−susceptible isolates (% NS) represents the number of patients with blood culture positive for non−susceptible isolates (numerator) over the total number of patients with blood culture positive for the organism and the organism was tested for susceptibility against the antibiotic (denominator).The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period.Grey bars indicate that testing with the antibiotic occurred for less than 70% of the total number of patients with blood culture positive for the organism.CI = confidence interval; NA = Not available/reported/tested; Methicillin: methicillin, oxacillin, or cefoxitin Proportion of non−susceptible isolates (% NS) represents the number of patients with blood culture positive for non−susceptible isolates (numerator) over the total number of patients with blood culture positive for the organism and the organism was tested for susceptibility against the antibiotic (denominator).The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period.Grey bars indicate that testing with the antibiotic occurred for less than 70% of the total number of blood culture positive for the organism.CI = confidence interval; NA = Not available/reported/tested; 3GC = 3rd−generation cephalosporin; FLUOROQUINOLONES: ciprofloxacin or levofloxacin; CARBAPENEMS: imipenem, meropenem, ertapenem or doripenem Proportion of non−susceptible isolates (% NS) represents the number of patients with blood culture positive for non−susceptible isolates (numerator) over the total number of patients with blood culture positive for the organism and the organism was tested for susceptibility against the antibiotic (denominator).The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period.Grey bars indicate that testing with the antibiotic occurred for less than 70% of the total number of blood culture positive for the organism.CI = confidence interval; NA = Not available/reported/tested; 3GC = 3rd−generation cephalosporin; FLUOROQUINOLONES: ciprofloxacin or levofloxacin; CARBAPENEMS: imipenem, meropenem, ertapenem or doripenem Proportion of non−susceptible isolates (% NS) represents the number of patients with blood culture positive for non−susceptible isolates (numerator) over the total number of patients with blood culture positive for the organism and the organism was tested for susceptibility against the antibiotic (denominator).The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period.Grey bars indicate that testing with the antibiotic occurred for less than 70% of the total number of blood culture positive for the organism.CI = confidence interval; NA = Not available/reported/tested; 3GC = 3rd−generation cephalosporin; FLUOROQUINOLONES: ciprofloxacin or levofloxacin; CARBAPENEMS: imipenem, meropenem, ertapenem or doripenem Proportion of non−susceptible isolates (% NS) represents the number of patients with blood culture positive for non−susceptible isolates (numerator) over the total number of patients with blood culture positive for the organism and the organism was tested for susceptibility against the antibiotic (denominator).The AMASS application de−duplicated the data by including only the first isolate per patient per specimen type per evaluation period.Grey bars indicate that testing with the antibiotic occurred for less than 70% of the total number of blood culture positive for the organism.CI = confidence interval; NA = Not available/reported/tested; AMINOGLYCOSIDES: either gentamicin or amikacin; CARBAPENEMS: imipenem, meropenem, ertapenem or doripenem − Acinetobacter spp.The eight organisms and antibiotics included in the report were selected based on the global priority list of antibiotic resistant bacteria and Global Antimicrobial Resistance Surveillance System (GLASS) of WHO[1,2].

Section [2]: Isolate−based surveillance report Introduction An
isolate−based surveillance report is generated by default, even if the hospital_ admission_data file is unavailable.This is to enable hospitals with only microbiology data available to utilize the de−duplication and report generation functions of AMASS.This report is without stratification by origin of infection.The report generated by the AMASS application version 1.0 includes only blood samples.The next version of AMASS will include other specimen types, including cerebrospinal fluid (CSF), urine, stool, and other specimens.
ResultsThe microbiology_data file had:Sample collection dates ranged from

01 Jan 1995 to 31 Jan 1995 Number
of records of blood specimens collected within the above date range: 81 blood specimens records Number of records of blood specimens with *negative culture (no growth): 0 blood specimens records Number of records of blood specimens with culture positive for a microorganism: 81 blood specimens records Number of records of blood specimens with culture positive for organism under this survey: 28 blood specimens records AMASS application is being developed by Cherry Lim, Clare Ling, Elizabeth Ashley, Paul Turner, Rahul Batra, Rogier van Doorn, Soawapak Hinjoy, Sopon Iamsirithaworn, Susanna Dunachie, Tri Wangrangsimakul, Viriya Hantrakun, William Schilling, John Stelling, Jonathan Edgeworth, Guy Thwaites, Nicholas PJ Day, Ben Cooper and Direk Limmathurotskul.The AMASS application was funded by the Wellcome Trust (grant no.206736 and 101103).C.L. is funded by a Training Research Fellowship (grant no.206736) and D.L. is funded by an Intermediate Training Fellowship (grant no.101103) from the Wellcome Trust.