{"status":"OK","data":{"id":356643,"identifier":"5QDGXR","persistentUrl":"https://doi.org/10.34894/5QDGXR","protocol":"doi","authority":"10.34894","separator":"/","publisher":"DataverseNL","publicationDate":"2024-06-17","storageIdentifier":"surf://10.34894/5QDGXR","guestbookId":101,"effectiveDatasetFileCountLimit":10000,"datasetFileUploadsAvailable":9990,"datasetType":"dataset","locks":[],"latestVersion":{"id":28433,"datasetId":356643,"datasetPersistentId":"doi:10.34894/5QDGXR","datasetType":"dataset","storageIdentifier":"surf://10.34894/5QDGXR","versionNumber":1,"internalVersionNumber":7,"versionMinorNumber":2,"versionState":"RELEASED","latestVersionPublishingState":"DRAFT","deaccessionLink":"","lastUpdateTime":"2024-06-24T11:33:21Z","releaseTime":"2024-06-24T11:33:21Z","createTime":"2024-06-24T11:33:03Z","publicationDate":"2024-06-17","citationDate":"2024-06-17","guestbookId":101,"effectiveDatasetFileCountLimit":10000,"datasetFileUploadsAvailable":9990,"termsOfUse":"The standard Data Sharing Agreement (DSA) of the UMC Utrecht must be signed without adjustments. This DSA is in compliance with Dutch law. No costs are involved.","confidentialityDeclaration":"no","specialPermissions":"To obtain access to the data, a <a href=\"https://www.umcutrecht.nl/en/data-request-form-umc-utrecht\">request form</a> has to be completed. In addition to a completed request form, a  Data Sharing Agreement (DSA) in line with GDPR regulations and/or a Research Collaboration Agreement (RCA) should be signed before data is shared. Only data requests in line with the Terms of Use will be taken into consideration. ","restrictions":"See Data Sharing Agreement.","citationRequirements":"See Data Sharing Agreement.","conditions":"To access and use the dataset please read the Terms of Use and the Terms of Access.","disclaimer":"See Data Sharing Agreement.","termsOfAccess":"By downloading or otherwise accessing the materials, the downloader represents his/her acceptance of the Terms of Use. ","fileAccessRequest":true,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Replication Data for: \"New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation\""},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Loots"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"UMC Utrecht"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0003-4984-0484"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"data management"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"UMC Utrecht"}},{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Loots"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"UMC Utrecht"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"Background: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs.\n\nAim: To develop and validate a sepsis prediction model for adult patients in primary care.\n\nDesign and setting: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020.\n\nMethod: Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations.\n\nResults: A total of 357 patients were included with a median age of 80 years (interquartile range 71–86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation.\n\nConclusion: Based on this study’s GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters."}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Sepsis; Diagnosis; General Practice"},"keywordVocabulary":{"typeName":"keywordVocabulary","multiple":false,"typeClass":"primitive","value":"MeSH"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Loots FJ, Smits M, Hopstaken RM, Jenniskens K, Schroeten FH, van den Bruel A, van de Pol AC, Oosterheert JJ, Bouma H, Little P, Moore M, van Delft S, Rijpsma D, Holkenborg J, van Bussel BC, Laven R, Bergmans DC, Hoogerwerf JJ, Latten GH, de Bont EG, Giesen P, Harder AD, Kusters R, van Zanten AR, Verheij TJ. New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation. Br J Gen Pract. 2022 May 26;72(719):e437-e445. doi: 10.3399/BJGP.2021.0520. PMID: 35440467; PMCID: PMC9037184."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.3399/BJGP.2021.0520"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://bjgp.org/content/72/719/e437"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Loots, Feike"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2023-02-10"}]},"dansDataVaultMetadata":{"displayName":"Data Vault Metadata","name":"dansDataVaultMetadata","fields":[{"typeName":"dansDataversePid","multiple":false,"typeClass":"primitive","value":"doi:10.34894/5QDGXR"},{"typeName":"dansDataversePidVersion","multiple":false,"typeClass":"primitive","value":"1.2"},{"typeName":"dansBagId","multiple":false,"typeClass":"primitive","value":"urn:uuid:3c1e2768-d263-4777-81db-f2a0000958fd"},{"typeName":"dansNbn","multiple":false,"typeClass":"primitive","value":"urn:nbn:nl:ui:13-5f8b9709-5cd6-4e89-9f1a-c102a8fe43cd"}]}},"files":[{"description":"Codebook","label":"codebook_BJGP_2022_Loots_FJ.xlsx","restricted":true,"version":1,"datasetVersionId":28433,"dataFile":{"id":356651,"persistentId":"","filename":"codebook_BJGP_2022_Loots_FJ.xlsx","contentType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","friendlyType":"MS Excel Spreadsheet","filesize":12051,"description":"Codebook","storageIdentifier":"surf://store:1863b5ef099-41f5553da92a","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"0b668bd2a59b68c44212ebc9c95856dd6eb6e559"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"Case Report Form","label":"CRF_TESDIT.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356652,"persistentId":"","filename":"CRF_TESDIT.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":259059,"description":"Case Report Form","storageIdentifier":"surf://store:1863b5ef165-e38e27b2a223","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"7902b2966974ab963b9deb65ddc7d431dbb925b5"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"Data Management Plan","label":"Data Management Plan TeSD-IT 1.0_getekend.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356645,"persistentId":"","filename":"Data Management Plan TeSD-IT 1.0_getekend.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":318019,"description":"Data Management Plan","storageIdentifier":"surf://store:1863b5ef260-ec856b0920f7","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"1171cfa648ec9f0324342bb37c97d07297d4a4a8"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"METC approval","label":"METC_approval_TESDIT.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356644,"persistentId":"","filename":"METC_approval_TESDIT.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":158729,"description":"METC approval","storageIdentifier":"surf://store:1863b5ef319-114558066966","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"3c65c8e257b9a96e861fbfb485292ba675d2c67c"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"Patient informed consent form example","label":"PIF_TESDIT.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356649,"persistentId":"","filename":"PIF_TESDIT.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":176781,"description":"Patient informed consent form example","storageIdentifier":"surf://store:1863b5ef3cf-1b350bda1dc4","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"e24c6cf60d0caaba0017c32115d450962af24757"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"Study protocol","label":"Protocol_TESDIT.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356646,"persistentId":"","filename":"Protocol_TESDIT.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":1412158,"description":"Study protocol","storageIdentifier":"surf://store:1863b5ef511-5d95a9619e07","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"ac9a373a83a1ccecf0151421f4738176601fd513"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"Manuscript","label":"Publication_BJGP_Loots_FJ.pdf","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356648,"persistentId":"","filename":"Publication_BJGP_Loots_FJ.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":189525,"description":"Manuscript","storageIdentifier":"surf://store:1863b5ef5c7-8b27ea1626c1","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"d959f94c5da13df31c8366ce86fbd54cbfc0e863"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"readme file","label":"readme.docx","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":425551,"persistentId":"","filename":"readme.docx","contentType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","friendlyType":"MS Word","filesize":19300,"description":"readme file","storageIdentifier":"surf://store:1901694e221-69b33aebbae1","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"1d4b16ef5ca34282f067ce2bfb9fcb0ce36b24e3"},"tabularData":false,"creationDate":"2024-06-14","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"R script sensitivity analyses","label":"Rscript_sensitivity_analyses_TESDIT_BJGP.R","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356647,"persistentId":"","filename":"Rscript_sensitivity_analyses_TESDIT_BJGP.R","contentType":"type/x-r-syntax","friendlyType":"R Syntax","filesize":16009,"description":"R script sensitivity analyses","storageIdentifier":"surf://store:1863b5ef678-937c55a09c43","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"184866020ff00cd87a03e1a8ed2036d45540cc1e"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}},{"description":"R script main analyses","label":"Rscript_TESDIT_BJGP.R","restricted":false,"version":1,"datasetVersionId":28433,"dataFile":{"id":356650,"persistentId":"","filename":"Rscript_TESDIT_BJGP.R","contentType":"type/x-r-syntax","friendlyType":"R Syntax","filesize":94934,"description":"R script main analyses","storageIdentifier":"surf://store:1863b5ef77f-e9a091556fe5","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"15e3f80a1602982206831ce2541ef6d8a6c860f3"},"tabularData":false,"creationDate":"2023-02-10","publicationDate":"2024-06-17","lastUpdateTime":"2024-06-24T11:33:21Z","fileAccessRequest":true}}]}}}