Predicting cost of care using self-reported health status data

BACKGROUND: We examined whether self-reported employee health status data can improve the performance of administrative data-based models for predicting future high health costs, and develop a predictive model for predicting new high cost individuals. METHODS: This retrospective cohort study used data from 8,917 Safeway employees self-insured by Safeway during 2008 and 2009. We created models using step-wise multivariable logistic regression starting with health services use data, then socio-demographic data, and finally adding the self-reported health status data to the model. RESULTS: Adding self-reported health data to the baseline model that included only administrative data (health services use and demographic variables; c-statistic = 0.63) increased the model” predictive power (c-statistic = 0.70). Risk factors associated with being a new high cost individual in 2009 were: 1) had one or more ED visits in 2008 (adjusted OR: 1.87, 95 % CI: 1.52, 2.30), 2) had one or more hospitalizations in 2008 (adjusted OR: 1.95, 95 % CI: 1.38, 2.77), 3) being female (adjusted OR: 1.34, 95 % CI: 1.16, 1.55), 4) increasing age (compared with age 18-35, adjusted OR for 36-49 years: 1.28; 95 % CI: 1.03, 1.60; adjusted OR for 50-64 years: 1.92, 95 % CI: 1.55, 2.39; adjusted OR for 65+ years: 3.75, 95 % CI: 2.67, 2.23), 5) the presence of self-reported depression (adjusted OR: 1.53, 95 % CI: 1.29, 1.81), 6) chronic pain (adjusted OR: 2.22, 95 % CI: 1.81, 2.72), 7) diabetes (adjusted OR: 1.73, 95 % CI: 1.35, 2.23), 8) high blood pressure (adjusted OR: 1.42, 95 % CI: 1.21, 1.67), and 9) above average BMI (adjusted OR: 1.20, 95 % CI: 1.04, 1.38). DISCUSSION: The comparison of the models between the full sample and the sample without theprevious high cost members indicated significant differences in the predictors. This has importantimplications for models using only the health service use (administrative data) given that the past high costis significantly correlated with future high cost and often drive the predictive models. CONCLUSIONS: Self-reported health data improved the ability of our model to identify individuals at risk for being high cost beyond what was possible with administrative data alone

Redefining Frequent Emergency Department Users

Frequent ED users are perceived to be a costly population that often abuse or misuse ED services due to a combination of unmet social needs and medical conditions that, in theory, could be treated outside of the ED at a lower cost. The reality is that factors contributing to frequent ED use are more varied and complex than originally believed

Cell size influences inorganic carbon acquisition in artificially selected phytoplankton

Cell size influences the rate at which phytoplankton assimilate dissolved inorganic carbon (DIC), but it is unclear whether volume-specific carbon uptake should be greater in smaller or larger cells. On the one hand, Fick's Law predicts smaller cells to have a superior diffusive CO2 supply. On the other, larger cells may have greater scope to invest metabolic energy to upregulate active transport per unit area through CO2 -concentrating mechanisms (CCMs). Previous studies have focused on among-species comparisons, which complicates disentangling the role of cell size from other covarying traits. In this study, we investigated the DIC assimilation of the green alga Dunaliella tertiolecta after using artificial selection to evolve a 9.3-fold difference in cell volume. We compared CO2 affinity, external carbonic anhydrase (CAext ), isotopic signatures (δ13 C) and growth among size-selected lineages. Evolving cells to larger sizes led to an upregulation of CCMs that improved the DIC uptake of this species, with higher CO2 affinity, higher CAext and higher δ13 C. Larger cells also achieved faster growth and higher maximum biovolume densities. We showed that evolutionary shifts in cell size can alter the efficiency of DIC uptake systems to influence the fitness of a phytoplankton species

An intervention to improve care and reduce costs for high-risk patients with frequent hospital admissions: a pilot study

<p>Abstract</p> <p>Background</p> <p>A small percentage of high-risk patients accounts for a large proportion of Medicaid spending in the United States, which has become an urgent policy issue. Our objective was to pilot a novel patient-centered intervention for high-risk patients with frequent hospital admissions to determine its potential to improve care and reduce costs.</p> <p>Methods</p> <p>Community and hospital-based care management and coordination intervention with pre-post analysis of health care utilization. We enrolled Medicaid fee-for-service patients aged 18-64 who were admitted to an urban public hospital and identified as being at high risk for hospital readmission by a validated predictive algorithm. Enrolled patients were evaluated using qualitative and quantitative interview techniques to identify needs such as transportation to/advocacy during medical appointments, mental health/substance use treatment, and home visits. A community housing partner initiated housing applications in-hospital for homeless patients. Care managers facilitated appropriate discharge plans then worked closely with patients in the community using a harm reduction approach.</p> <p>Results</p> <p>Nineteen patients were enrolled; all were male, 18/19 were substance users, and 17/19 were homeless. Patients had a total of 64 inpatient admissions in the 12 months before the intervention, versus 40 in the following 12 months, a 37.5% reduction. Most patients (73.3%) had fewer inpatient admissions in the year after the intervention compared to the prior year. Overall ED visits also decreased after study enrollment, while outpatient clinic visits increased. Yearly study hospital Medicaid reimbursements fell an average of $16,383 per patient.</p> <p>Conclusions</p> <p>A pilot intervention for high-cost patients shows promising results for health services usage. We are currently expanding our model to serve more patients at additional hospitals to see if the pilot's success can be replicated.</p> <p>Trial registration</p> <p>Clinicaltrials.gov Identifier: <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1292096">NCT01292096</a></p

Who with suspected prostate cancer can benefit from Proclarix after multiparametric magnetic resonance imaging?

Cathepsin D; Magnetic resonance imaging; ProclarixCatepsina D; Imatges per ressonància magnètica; ProclarixCatepsina D; Imágenes por resonancia magnética; ProclarixProclarix is a new blood-based test to assess the likelihood of clinically significant prostate cancer (csPCa) defined as >2 grade group. In this study, we analyzed whether Proclarix and PSA density (PSAD) could improve the selection of candidates for prostate biopsy after multiparametric magnetic resonance imaging (mpMRI). Proclarix and PSAD were assessed in 567 consecutive men with suspected PCa in whom pre-biopsy 3 Tesla mpMRI, scoring with Prostate Imaging-Report and Data System (PI-RADS) v.2, and guided and/or systematic biopsies were performed. Proclarix and PSAD thresholds having csPCa sensitivity over 90% were found at 10% and 0.07 ng/(mL*cm3), respectively. Among 100 men with negative mpMRI (PI-RADS <3), csPCa was detected in 6 cases, which would have been undetected if systematic biopsies were avoided. However, Proclarix suggested performing a biopsy on 70% of men with negative mpMRI. In contrast, PSAD only detected 50% of csPCa and required 71% of prostate biopsies. In 169 men with PI-RADS 3, Proclarix avoided 21.3% of prostate biopsies and detected all 25 cases of csPCa, while PSAD avoided 26.3% of biopsies, but missed 16% of csPCa. In 190 men with PI-RADS 4 and 108 with PI-RADS 5, Proclarix avoided 12.1% and 5.6% of prostate biopsies, but missed 4.8% and 1% of csPCa, respectively. PSAD avoided 18.4% and 9.3% of biopsies, but missed 11.4% and 4.2% csPCa, respectively. We conclude that Proclarix outperformed PSAD in the selection of candidates for prostate biopsy, especially in men with PI-RADS <3.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Instituto de Salud Carlos III, (grant number PI20/01666)

The Efficacy of Proclarix to Select Appropriate Candidates for Magnetic Resonance Imaging and Derived Prostate Biopsies in Men with Suspected Prostate Cancer

Diagnosis; Proclarix; Prostate cancerDiagnóstico; Proclarix; Cáncer de próstataDiagnòstic; Proclarix; Càncer de pròstataPurpose To analyze how Proclarix is valuable to appropriately select candidates for multiparametric magnetic resonance imaging (mpMRI) and derived biopsies, among men with suspected prostate cancer (PCa). Proclarix is a new marker computing the clinically significant PCa (csPCa) risk, based on serum thosmbospondin-1, cathepsin D, prostate-specific antigen (PSA) and percent free PSA, in addition to age, that has been developed in men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and normal digital rectal examination (DRE). Materials and Methods Proclarix score (0%–100%) is analyzed in a prospective frozen serum collection of 517 correlative men scheduled for guided and/or systematic biopsies after mpMRI. Outcome variables were csPCa detection (grade group ≥2), insignificant PCa (iPCa) overdetection and avoided mpMRIs. Results The area under the curve of Proclarix was 0.701 (95% CI 0.637–0.765) among 281 men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and -normal DRE, and 0.754 (95% CI 0.701–0.807) in the others, p=0.038. Net benefit of Proclarix existed in all men. After selecting 10% threshold, Proclarix was integrated in an algorithm which also used the serum PSA level and DRE. A reduction of 25.4% of mpMRIs request was observed and 17.7% of prostate biopsies. Overdetection of iPCa was reduced in 18.2% and 2.6% of csPCa were misdiagnosed. Conclusions Proclarix is valuable in all men with suspected PCa. An algorithm integrating Proclarix score, serum PSA, and DRE can avoid mpMRI requests, unnecessary prostate biopsies and iPCa overdetection, with minimal loss of csPCa detection

Update in collecting duct carcinoma: Current aspects of the clinical and molecular characterization of an orphan disease

Bellini carcinoma; Clear-cell carcinoma; Collecting ductCarcinoma de Bellini; Carcinoma de cèl·lules clares; Conducte col·lectorCarcinoma de Bellini; Carcinoma de células claras; Conducto colectorCollecting duct renal cell carcinoma (cdRCC), which until recently was thought to arise from the collecting ducts of Bellini in the renal medulla, is a rare and aggressive type of non-clear renal cell carcinoma (ncRCC), accounting for 1% of all renal tumors and with nearly 50% of patients being diagnosed with Stage IV disease. The median overall survival in this setting is less than 12 months. Several regimens of chemotherapies had been used based on morphologic and cytogenetic similarities with urothelial cell carcinoma described previously, although the prognosis still remains poor. The use of targeted therapies also did not result in favorable outcomes. Recent works using NGS have highlighted genomic alterations in SETD2, CDKN2A, SMARCB1, and NF2. Moreover, transcriptomic studies have confirmed the differences between urothelial carcinoma and cdRCC, the possible true origin of this disease in the distal convoluted tubule (DCT), differentiating from other RCC (e.g., clear cell and papillary) that derive from the proximal convoluted tubule (PCT), and enrichment in immune cells that may harbor insights in novel treatment strategies with immunotherapy and target agents. In this review, we update the current aspects of the clinical, molecular characterization, and new targeted therapeutic options for Collecting duct carcinoma and highlight the future perspectives of treatment in this setting

Comparison of Proclarix, PSA Density and MRI-ERSPC Risk Calculator to Select Patients for Prostate Biopsy after mpMRI

Proclarix; Clinically significant prostate cancer; Magnetic resonance imagingProclarix; Càncer de pròstata clínicament significatiu; Imatges per ressonància magnèticaProclarix; Cáncer de próstata clínicamente significativo; Imágenes por resonancia magnéticaTools to properly select candidates for prostate biopsy after magnetic resonance imaging (MRI) have usually been analyzed in overall populations with suspected prostate cancer (PCa). However, the performance of these tools can change regarding the Prostate Imaging-Reporting and Data System (PI-RADS) categories due to the different incidence of clinically significant PCa (csPCa). The objective of the study was to analyze PSA density (PSAD), MRI-ERSPC risk calculator (RC), and Proclarix to properly select candidates for prostate biopsy regarding PI-RADS categories. We performed a head-to-head analysis of 567 men with suspected PCa, PSA > 3 ng/mL and/or abnormal rectal examination, in whom two to four core transrectal ultrasound (TRUS) guided biopsies to PI-RADS ≥ three lesions and/or 12-core TRUS systematic biopsies were performed after 3-tesla mpMRI between January 2018 and March 2020 in one academic institution. The overall detection of csPCa was 40.9% (6% in PI-RADS 3, although none of them exhibited 100% sensitivity for csPCa in this setting. Therefore, tools to properly select candidates for prostate biopsy after MRI must be analyzed regarding the PI-RADS categories. While MRI-ERSPC RC outperformed PSAD and Proclarix in the overall population, Proclarix outperformed in PI-RADS ≤ 3, and no tool guaranteed 100% detection of csPCa in PI-RADS 4 and 5

Improving the Early Detection of Clinically Significant Prostate Cancer in Men in the Challenging Prostate Imaging-Reporting and Data System 3 Category

Clinically significant prostate cancer; Prostate Cancer predictive model; Multiparametric magnetic resonance imagingCáncer de próstata clínicamente significativo; Modelo predictivo de cáncer de próstata; Resonancia magnética multiparamétricaCàncer de pròstata clínicament significatiu; Model predictiu del càncer de pròstata; Imatge de ressonància magnètica multiparamètricaBackground Prostate Imaging-Reporting and Data System (PI-RADS) category 3 is a challenging scenario for detection of clinically significant prostate cancer (csPCa) and some tools can improve the selection of appropriate candidates for prostate biopsy. Objective To assess the performance of the European Randomized Study of Screening for Prostate Cancer (ERSPC) magnetic resonance imaging (MRI) model, the new Proclarix test, and prostate-specific antigen density (PSAD) in selecting candidates for prostate biopsy among men in the PI-RADS 3 category. Design, setting, and participants We conducted a head-to-head prospective analysis of 567 men suspected of having PCa for whom guided and systematic biopsies were scheduled between January 2018 and March 2020 in a single academic institution. A PI-RADS v.2 category 3 lesion was identified in 169 men (29.8%). Outcome measurement and statistical analysis csPCa, insignificant PCa (iPCa), and unnecessary biopsy rates were analysed. csPCa was defined as grade group ≥2. Receiver operating characteristic (ROC) curves, decision curve analysis curves, and clinical utility curves were plotted. Results and limitations PCa was detected in 53/169 men (31.4%) with a PI-RADS 3 lesion, identified as csPCa in 25 (14.8%) and iPCa in 28 (16.6%). The area under the ROC curve for csPCa detection was 0.703 (95% confidence interval [CI] 0.621–0.768) for Proclarix, 0.657 (95% CI 0.547–0.766) for the ERSPC MRI model, and 0.612 (95% CI 0.497–0.727) for PSAD (p = 0.027). The threshold with the highest sensitivity was 10% for Proclarix, 1.5% for the ERSPC MRI model, and 0.07 ng/ml/cm3 for PSAD, which yielded sensitivity of 100%, 91%, and 84%, respectively. Some 21.3%, 26.2%, and 7.1% of biopsies would be avoided with Proclarix, PSAD, and the ERSPC MRI model, respectively. Proclarix showed a net benefit over PSAD and the ERSPC MRI model. Both Proclarix and PSAD reduced iPCa overdetection from 16.6% to 11.3%, while the ERSPC MRI model reduced iPCa overdetection to 15.4%. Conclusions Proclarix was more accurate in selecting appropriate candidates for prostate biopsy among men in the PI-RADS 3 category when compared to PSAD and the ERSPC MRI model. Proclarix detected 100% of csPCa cases and would reduce prostate biopsies by 21.3% and iPCa overdetection by 5.3%

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

Clinically significant prostate cancer; Predictive model; Prostate-specific antigenCàncer de pròstata clínicament significatiu; Model predictiu; Antigen específic de la pròstataCáncer de próstata clínicamente significativo; Modelo predictivo; Antígeno específico de la próstataThis study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880–0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774–0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL2 for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy.This research was funded by Instituto de Salut Carlos III (ESP), grant number PI20/01666