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1.
BMC Health Serv Res ; 22(1): 36, 2022 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-34991558

RESUMO

BACKGROUND: Patients with back pain are often in contact with 2-4 hospital departments when receiving a back pain diagnosis and treatment. This complicates the entire clinical course description. There is, currently, no model that describes the course across departments for patients with back pain. This study aims to construct an interdisciplinary clinical course using the central register's information. METHODS: All patients with back pain referred for diagnosis and treatment at the Spine Center of Southern Denmark from 1 January 2011 until 31 December 2017 were included. By means of information available in central registers, we described the interdisciplinary clinical course for the individual patient, including information on all contacts at different departments, and proposed three different models to define the index and final date. The index date was defined as the first visit without a previous contact to the Spine Center for 6 months for model I, 1 year for model II, and 2 years for model III. The final date was defined as the last visit without following contacts for 6 months, 1 year, and 2 years, respectively, for models I, II, and III. RESULTS: A total of 69,564 patients (male: n = 30,976) with back pain diagnosis were identified. The three models all leave the information on the entire course at the hospital. In model I (64,757 clinical back pain courses), the time span to a possible previous clinical course is too short to secure the start of a new course (14% had two or more). With at least 1 year between a possible previous contact, model II (60,914 courses) fits the everyday clinical practice (9% had two or more clinical back pain courses). In model III (60,173 courses) it seems that two independent courses might be connected in the same course as only 5% had two or more clinical back pain courses. CONCLUSIONS: Despite contact with different departments, the clinical course for back pain patients can be described by information from the central registers. A one-year time interval fits best the clinicians' everyday observations.


Assuntos
Dor Lombar , Dor nas Costas/diagnóstico , Dor nas Costas/epidemiologia , Dor nas Costas/terapia , Dinamarca/epidemiologia , Humanos , Dor Lombar/diagnóstico , Dor Lombar/epidemiologia , Dor Lombar/terapia , Masculino , Encaminhamento e Consulta , Sistema de Registros
2.
Hosp Pract (1995) ; 48(4): 223-229, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32484370

RESUMO

OBJECTIVES: Many factors contribute to the plasma albumin (PA) level. We aimed to quantify different factors' relative contribution to the PA level when diagnosing hematological malignancy (HM). METHODS: The study was a population-based registry study including patients with HM in a Danish region. We applied multivariate linear regression analyses with C-reactive protein (CRP), WHO performance score (WHO-PS), age, sex, comorbidity, and HM type as exposures and the PA level on the day of the HM diagnosis (DX) as the outcome. The relative contribution of each exposure was determined as a percentage of the models' coefficient of determination (R2). RESULTS: In total, 2528 patients with HM had PA measured on DX. In the model comprising all exposures, CRP contributed with 65.8% to the R2 of 0.389 whereas 3 variables (CRP, WHO-PS, HM type) together contributed with 96.1%. When CRP was excluded from the model, R2 declined to 0.215 and the WHO-PS contributed with 96%. Other models, including separate analyses for each HM type, corroborated these results, except in myeloma patients where WHO-PS contributed with 61.1% to the R2 of 0.234. CONCLUSION: The inflammation biomarker CRP was the main predictor of the PA level on DX. The WHO-PS also contributed to the PA level on DX whereas the remaining factors (HM type, age, sex, and comorbidity) were of much less importance.


Assuntos
Neoplasias Hematológicas/sangue , Albumina Sérica/análise , Adolescente , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Biomarcadores , Índice de Massa Corporal , Proteína C-Reativa/análise , Comorbidade , Dinamarca/epidemiologia , Feminino , Neoplasias Hematológicas/classificação , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Análise Multivariada , Fatores Sexuais , Adulto Jovem
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