Healthcare professionals (HCPs) on the front lines against COVID-19 may face increased workload and stress. Understanding HCPs' risk for burnout is critical to supporting HCPs and maintaining the ...quality of healthcare during the pandemic.
To assess exposure, perceptions, workload, and possible burnout of HCPs during the COVID-19 pandemic we conducted a cross-sectional survey. The main outcomes and measures were HCPs' self-assessment of burnout, indicated by a single item measure of emotional exhaustion, and other experiences and attitudes associated with working during the COVID-19 pandemic.
A total of 2,707 HCPs from 60 countries participated in this study. Fifty-one percent of HCPs reported burnout. Burnout was associated with work impacting household activities (RR = 1·57, 95% CI = 1·39-1·78, P<0·001), feeling pushed beyond training (RR = 1·32, 95% CI = 1·20-1·47, P<0·001), exposure to COVID-19 patients (RR = 1·18, 95% CI = 1·05-1·32, P = 0·005), and making life prioritizing decisions (RR = 1·16, 95% CI = 1·02-1·31, P = 0·03). Adequate personal protective equipment (PPE) was protective against burnout (RR = 0·88, 95% CI = 0·79-0·97, P = 0·01). Burnout was higher in high-income countries (HICs) compared to low- and middle-income countries (LMICs) (RR = 1·18; 95% CI = 1·02-1·36, P = 0·018).
Burnout is present at higher than previously reported rates among HCPs working during the COVID-19 pandemic and is related to high workload, job stress, and time pressure, and limited organizational support. Current and future burnout among HCPs could be mitigated by actions from healthcare institutions and other governmental and non-governmental stakeholders aimed at potentially modifiable factors, including providing additional training, organizational support, and support for family, PPE, and mental health resources.
Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a ...consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.
Abstract Background The four-kallikrein panel and the Prostate Health Index (PHI) have been shown to improve prediction of prostate cancer (PCa) compared with prostate-specific antigen (PSA). No ...comparison of the four-kallikrein panel and PHI has been presented. Objective To compare the four-kallikrein panel and PHI for predicting PCa in an independent cohort. Design, setting, and participants Participants were from a population-based cohort of PSA-tested men in Stockholm County. We included 531 men with PSA levels between 3 and 15 ng/ml undergoing first-time prostate biopsy during 2010–2012. Outcome measurements and statistical analysis Models were fitted to case status. We computed calibration curves, the area under the receiver-operating characteristics curve (AUC), decision curves, and percentage of saved biopsies. Results and limitations The four-kallikrein panel showed AUCs of 69.0 when predicting any-grade PCa and 71.8 when predicting high-grade cancer (Gleason score ≥7). Similar values were found for PHI: 70.4 and 71.1, respectively. Both models had higher AUCs than a base model with PSA value and age ( p < 0.0001 for both); differences between models were not significant. Sensitivity analyses including men with any PSA level or a previous biopsy did not materially affect our findings. Using 10% predicted risk of high-grade PCa by the four-kallikrein panel or PHI of 39 as cut-off for biopsy saved 29% of performed biopsies at a cost of delayed diagnosis for 10% of the men with high-grade cancers. Both models showed limited net benefit in decision analysis. The main study limitation was lack of digital rectal examination data and biopsy decision being based on PSA information. Conclusions The four-kallikrein panel and PHI similarly improved discrimination when predicting PCa and high-grade PCa. Both are simple blood tests that can reduce the number of unnecessary biopsies compared with screening with total PSA, representing an important new option to reduce harm. Patient summary Prostate-specific antigen screening is controversial due to limitations of the test. We found that two blood tests, the Prostate Health Index and the four-kallikrein panel, performed similarly and could both aid in decision making among Swedish men undergoing a prostate biopsy.
Summary Background The prostate-specific antigen (PSA) test is used to screen for prostate cancer but has a high false-positive rate that translates into unnecessary prostate biopsies and ...overdiagnosis of low-risk prostate cancers. We aimed to develop and validate a model to identify high-risk prostate cancer (with a Gleason score of at least 7) with better test characteristics than that provided by PSA screening alone. Methods The Stockholm 3 (STHLM3) study is a prospective, population-based, paired, screen-positive, diagnostic study of men without prostate cancer aged 50–69 years randomly invited by date of birth from the Swedish Population Register kept by the Swedish Tax Agency. Men with prostate cancer at enrolment were excluded from the study. The predefined STHLM3 model (a combination of plasma protein biomarkers PSA, free PSA, intact PSA, hK2, MSMB, MIC1, genetic polymorphisms 232 SNPs, and clinical variables age, family, history, previous prostate biopsy, prostate exam), and PSA concentration were both tested in all participants enrolled. The primary aim was to increase the specificity compared with PSA without decreasing the sensitivity to diagnose high-risk prostate cancer. The primary outcomes were number of detected high-risk cancers (sensitivity) and the number of performed prostate biopsies (specificity). The STHLM3 training cohort was used to train the STHLM3 model, which was prospectively tested in the STHLM3 validation cohort. Logistic regression was used to test for associations between biomarkers and clinical variables and prostate cancer with a Gleason score of at least 7. This study is registered with ISCRTN.com , number ISRCTN84445406. Findings The STHLM3 model performed significantly better than PSA alone for detection of cancers with a Gleason score of at least 7 (p<0·0001), the area under the curve was 0·56 (95% CI 0·55–0·60) with PSA alone and 0·74 (95% CI 0·72–0·75) with the STHLM3 model. All variables used in the STHLM3 model were significantly associated with prostate cancers with a Gleason score of at least 7 (p<0·05) in a multiple logistic regression model. At the same level of sensitivity as the PSA test using a cutoff of ≥3 ng/mL to diagnose high risk prostate cancer, use of the STHLM3 model could reduce the number of biopsies by 32% (95% CI 24–39) and could avoid 44% (35–54) of benign biopsies. Interpretation The STHLM3 model could reduce unnecessary biopsies without compromising the ability to diagnose prostate cancer with a Gleason score of at least 7, and could be a step towards personalised risk-based prostate cancer diagnostic programmes. Funding Stockholm County Council (Stockholms Läns Landsting).
A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening.
To perform an external ...evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists.
This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer).
Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%).
The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level.
To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.
Ongoing controversy over the optimal approach to breast cancer screening has led to discordant professional society recommendations, particularly in women age 40 to 49 years. One potential solution ...is risk-based screening, where decisions around the starting age, stopping age, frequency, and modality of screening are based on individual risk to maximize the early detection of aggressive cancers and minimize the harms of screening through optimal resource utilization. We present a novel approach to risk-based screening that integrates clinical risk factors, breast density, a polygenic risk score representing the cumulative effects of genetic variants, and sequencing for moderate- and high-penetrance germline mutations. We demonstrate how thresholds of absolute risk estimates generated by our prediction tools can be used to stratify women into different screening strategies (biennial mammography, annual mammography, annual mammography with adjunctive magnetic resonance imaging, defer screening at this time) while informing the starting age of screening for women age 40 to 49 years. Our risk thresholds and corresponding screening strategies are based on current evidence but need to be tested in clinical trials. The Women Informed to Screen Depending On Measures of risk (WISDOM) Study, a pragmatic, preference-tolerant randomized controlled trial of annual vs personalized screening, will study our proposed approach. WISDOM will evaluate the efficacy, safety, and acceptability of risk-based screening beginning in the fall of 2016. The adaptive design of this trial allows continued refinement of our risk thresholds as the trial progresses, and we discuss areas where we anticipate emerging evidence will impact our approach.
Several important lessons have been learnt from our experiences in screening for various cancers. Screening programmes for cervical and colorectal cancers have had the greatest success, probably ...because these cancers are relatively homogenous, slow-growing, and have identifiable precursors that can be detected and removed; however, identifying the true obligate precursors of invasive disease remains a challenge. With regard to screening for breast cancer and for prostate cancer, which focus on early detection of invasive cancer, preferential detection of slower-growing, localized cancers has occurred, which has led to concerns about overdiagnosis and overtreatment; programmes for early detection of invasive lung cancers are emerging, and have faced similar challenges. A crucial consideration in screening for breast, prostate, and lung cancers is their remarkable phenotypic heterogeneity, ranging from indolent to highly aggressive. Efforts have been made to address the limitations of cancer-screening programmes, providing an opportunity for cross-disciplinary learning and further advancement of the science. Current innovations are aimed at identifying the individuals who are most likely to benefit from screening, increasing the yield of consequential cancers on screening and biopsy, and using molecular tests to improve our understanding of disease biology and to tailor treatment. We discuss each of these concepts and outline a dynamic framework for continuous improvements in the field of cancer screening.
The size and breadth of the data are of crucial importance for AI development and evaluation. A common definition of external validation of an AI system is that the validation of the system is done ...entirely independent of the training data—ie, the data used for evaluation does not contain patients that are present in the training data and are acquired from a different site on a different technical platform operated by different staff. ...MRI examinations with insufficient image quality were excluded from PI-CAI.
We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, ...and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection.
In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0·7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer.
7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0·3% (95% CI 0·0–4·3), or 2·6% (1·1–5·4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively.
Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later.
Stockholm City Council.
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