Parantumattoman syövän lääkehoito elämän loppuvaiheessa - hyötyä vai haittaa?

Edennyttä, parantumatonta syöpää sairastavan potilaan viimeisen linjan syöpälääkitys on usein heikkotehoista ja voi huonontaa potilaan elämänlaatua. Potilaiden odotukset ja käsitykset syövän lääkehoidon tehosta voivat olla epärealistisia. Hoitohenkilökunnalta vaaditaan tietoa ja erityisen hyviä vuorovaikutustaitoja hoitojen hyötyjen ja haittojen sanoittamisessa. Elämän rajallisuuden ja eksistentiaalisen hädän hyväksyminen sekä niiden kanssa toimeen tuleminen on tärkeää ammattilaisille ja potilaille. Valitettavasti potilaan ennustetta arvioivat työkalut eivät ole juurtuneet kliiniseen käyttöön. Syövän lääkehoito voi olla parantumattomasti sairaalle potilaalle arvokas toivon lähde, mutta ainoana toivon lähteenä sen ei tule toimia. Hyvän oireenmukaisen hoidon merkityksen korostaminen on tärkeää hoitoneuvotteluissa.</p

End-of-life decisions guiding the palliative care of cancer patients visiting emergency department in South Western Finland: A retrospective cohort study

BackgroundUntil recently, palliative care (PC) resources in Finland have been sparse. To meet the increasing need for PC an end-of-life (EOL) care project has been ongoing in South Western Finland since 2012, and in 2015, a weekday palliative outpatient clinic was established in Turku University Hospital (TUH). The aim of this study was to explore the effect of the project and the PC clinic on the management practices of EOL cancer patients attending the Emergency Department (ED) of TUH from 2013 to 2016.MethodsThe medical records of all cancer patients (ICD-10 codes C00–97) admitted to the ED of TUH between August 1–December 31, in 2013 and 2016, were analyzed: n = 529, n = 432 respectively (2013 and 2016). The analysis focused on those patients in EOL care; n = 77, n = 63, respectively. The late palliative patients were defined by PC decision, thus termination of life-prolonging cancer-specific treatments. The EOL patients were in the imminently dying phase of their illness. The site of referral after an ED visit was also verified together with the documentation on advance care plans (ACP), and the impact of palliative outpatient visits.ResultsIn 2016, the number of late palliative and EOL patients admitted to the ED has shown a tendency to decrease. The quality of the documentation for treatment goals, do-not-resuscitate (DNR) orders, living wills and connections to primary care providers has improved since 2013. Prior visits to palliative outpatient clinic correlated well with the more comprehensive ACP information: i) DNR order (p = 0.0001); ii) connection to primary care (p = 0.003); iii) documented ICD-10 code Z51.5 (p = 0.0001).ConclusionsEven modest investments in resources for PC can induce an objective change in the allocation of health care resources, and improve the ACP for the cancer patients at their EOL. A visit to a palliative outpatient clinic may offer one approach for improving the quality and completion of ACP documentation.</div

Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data

Background The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training.Methods Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital.Results Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint.Conclusions Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.</p

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

BackgroundPersistent 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 methodsThis 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.ResultsThe 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.ConclusionsDeep 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.</p

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Palliatiivisen hoidon ja saattohoidon säädösmuutosten kustannusvaikutusten arviointi : Laskentatyöryhmän raportti

STM:n asettama Elämän loppuvaiheen hoito -työryhmä on julkaissut kaksi selvitysraporttia (2019a, 2019b), joissa on esitetty suositukset elämän loppuvaiheen hoidon järjestämisestä Suomessa. Tässä raportissa esitetään laskelmia suositusten pohjalta esitettyjen toimintamallien ja lainsäädännön muutosehdotusten kustannusvaikutuksista. Taloudellisten vaikutusten arviointi kohdistetaan uusille tai laajeneville tehtäville. Kustannusten alueittainen jakautuminen riippuu siitä, ovatko tarkastelun kohteena erityisvastuu- vai hyvinvointialueet. Valtaosa odotetuista uusista menoista (n. 40 Me) koostuisi henkilöstön palkkauksesta (n. 33 Me). Sairaanhoitajien osuus olisi 370–418 henkilötyövuotta, lääkäreiden 46–53 ja psykologien 55 henkilötyövuotta. Perustettavien professuurien kustannuksiksi arvioitiin 0,5 Me. Ruotsin mallin mukainen saattohoitovapaata koskeva erityismääräraha tuottaisi noin 6-7 Me kustannukset. Tilamuutosten ja koulutuksen aiheuttamia kustannuksia ei arvioitu raportissa. Kustannussäästöjä on mahdollista saavuttaa omaksumalla valtakunnallisesti käytössä olevia alueiden parhaita käytäntöjä iäkkäiden palveluissa. Säästöt kumuloituvat ja hillitsevät merkittävästi kustannuskehitystä lähivuosikymmenien tulevaisuuden skenaariossa. Kustannusten hallinnassa oleellista on suositusten mukainen osaamistason nosto. Säästö tulee viiveellä ja edellyttää raportissa esitettyjä investointeja