Publications by authors named "A Karlsson"

1,032 Publications

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Pressure ulcer risk assessment-registered nurses´ experiences of using PURPOSE T: A focus group study.

J Clin Nurs 2021 Jun 9. Epub 2021 Jun 9.

Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden.

Aim: To evaluate the clinical usability of PURPOSE T among registered nurses in Sweden.

Background: Pressure ulcers are an adverse event and a problem worldwide. Risk assessment is a cornerstone, and a first step in pressure ulcer prevention is to identify possible risk patients and/or pressure ulcers. There are many pressure ulcer risk assessment instruments; however, they are not updated and/or evidence-based. PURPOSE T has been psychometrically evaluated in the UK and in Sweden with good inter-rater and test-retest reliability, and convergent validity was reported as moderate.

Design: A descriptive study design with a qualitative approach.

Methods: A total of six focus group interviews with 29 registered nurses were conducted. They were recruited from May 2018 to November 2018 from a university hospital and two nursing homes in Sweden. Data analysis was performed as described by Krueger. The study adheres to the COREQ guidelines.

Results: Four categories were identified: "An efficient risk assessment instrument performed at the bedside," "Deeper understanding and awareness of risk factors," "Benefits compared to the Modified Norton Scale" and "Necessity of integration of PURPOSE T in the electronic health record and team collaboration."

Conclusion: The registered nurses acknowledged an overall positive perception of PURPOSE T´s clinical usability. Future research is needed to evaluate the feasibility of PURPOSE T.

Relevance To Clinical Practice: PURPOSE T has the potential to replace outdated pressure ulcers risk assessment instruments that are used today.
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http://dx.doi.org/10.1111/jocn.15901DOI Listing
June 2021

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit.

ESMO Open 2021 Jun 3;6(3):100175. Epub 2021 Jun 3.

University of Turku, Turku, Finland; Department of Oncology, Turku University Hospital, Turku, Finland; FICAN West Cancer Centre, Turku, Finland. Electronic address:

Background: Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the 2020 Surgeon General's report, smoking cessation may reduce CM but supporting evidence is not wide. Use of deep learning-based modeling that enables universal natural language processing of medical narratives to acquire population-based real-life smoking data may help overcome the challenge. We assessed the effect of smoking status and within-1-year smoking cessation on CM by an in-house adapted freely available language processing algorithm.

Materials And Methods: This cross-sectional real-world study included 29 823 patients diagnosed with cancer in 2009-2018 in Southwest Finland. The medical narrative, International Classification of Diseases-10th edition codes, histology, cancer treatment records, and death certificates were combined. Over 162 000 sentences describing tobacco smoking behavior were analyzed with ULMFiT and BERT algorithms.

Results: The language model classified the smoking status of 23 031 patients. Recent quitters had reduced CM [hazard ratio (HR) 0.80 (0.74-0.87)] and OM [HR 0.78 (0.72-0.84)] compared to persistent smokers. Compared to never smokers, persistent smokers had increased CM in head and neck, gastro-esophageal, pancreatic, lung, prostate, and breast cancer and Hodgkin's lymphoma, irrespective of age, comorbidities, performance status, or presence of metastatic disease. Increased CM was also observed in smokers with colorectal cancer, men with melanoma or bladder cancer, and lymphoid and myeloid leukemia, but no longer independently of the abovementioned covariates. Specificity and sensitivity were 96%/96%, 98%/68%, and 88%/99% for never, former, and current smokers, respectively, being essentially the same with both models.

Conclusions: Deep learning can be used to classify large amounts of smoking data from the medical narrative with good accuracy. The results highlight the detrimental effects of persistent smoking in oncologic patients and emphasize that smoking cessation should always be an essential element of patient counseling.
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http://dx.doi.org/10.1016/j.esmoop.2021.100175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182259PMC
June 2021

Overall survival after initial radiotherapy for brain metastases; a population based study of 2140 patients with non-small cell lung cancer.

Acta Oncol 2021 May 25:1-7. Epub 2021 May 25.

Department of Oncology, Oslo University Hospital, Oslo, Norway.

Background: Brain metastases (BM) occur in about 30% of all patients with non-small cell lung cancer (NSCLC). BM treatment guidelines recommend more frequent use of stereotactic radiotherapy (SRT). Overall, studies report no difference in overall survival (OS) comparing SRT to whole-brain radiotherapy (WBRT). We examined survival after radiotherapy for BM in a population-based sample from the South-Eastern Norway Regional Health Authority treated 2006-2018.

Methods: We reviewed electronic medical records of 2140 NSCLC patients treated with SRT or WBRT for BM from 2006-2018. Overall survival (OS) was compared to predicted survival according to the prognostic systems DS-GPA and Lung-molGPA.

Results: Use of SRT increased during the period, from 19% (2006-2014) to 45% (2015-2018). Median OS for all patients was 3.0 months, increasing from 2.0 (2006) to 4.0 (2018). Median OS after SRT was 7.0 months ( = 435) and 3.0 months after WBRT ( = 1705). Twenty-seven percent of SRT patients and 50% of WBRT patients died within 90 days after start of RT. Age ≥70, male sex, KPS ≤70, non-adenocarcinoma histology, ECM present, multiple BM, and WBRT were associated with shorter survival ( < .001). Actual mOS corresponded best with predicted mOS by DS-GPA and Lung-molGPA for the SRT group.

Conclusion: Overall survival after radiotherapy (RT) for BM improved during the study period, but only for patients treated with SRT. Survival after WBRT remains poor; its use should be questioned. DS-GPA and Lung-molGPA seem most useful in predicting prognosis considered for SRT.
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http://dx.doi.org/10.1080/0284186X.2021.1924399DOI Listing
May 2021

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor.

Brief Bioinform 2021 May 10. Epub 2021 May 10.

Lund University, Sweden.

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.
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http://dx.doi.org/10.1093/bib/bbab154DOI Listing
May 2021

Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy.

Phys Imaging Radiat Oncol 2021 Jan 11;17:36-42. Epub 2021 Jan 11.

Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Background And Purpose: Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm.

Materials And Methods: For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume.

Results: For overall body, the results of the geometric validation were (Mean ± 1sd): Mean error -5 ± 10 HU, mean absolute error 67 ± 14 HU, Dice similarity coefficient 0.98 ± 0.05, and Hausdorff distance difference 4.2 ± 1.7 mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were -0.3 ± 3.4, 1.1 ± 2.0 and 0.7 ± 3.8 mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12 Gy). The absorbed doses were considered equivalent (p-value < 0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7-99.9%).

Conclusions: The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.
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http://dx.doi.org/10.1016/j.phro.2020.12.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058030PMC
January 2021