Prognosis of Gleason Score 9-10 Prostatic Adenocarcinoma in Needle Biopsies: A Nationwide Population-based Study.

BACKGROUND: Since 2014, prostate cancer is reported using five-tier grouping of Gleason scores. Studies have suggested prognostic heterogeneity within the groups. OBJECTIVE: We assessed the risk of prostate cancer death for men diagnosed with Gleason scores 4 + 5, 5 + 4, and 5 + 5 on needle biopsy in a population-based cohort. DESIGN, SETTING, AND PARTICIPANTS: We used the data from Prostate Cancer data Base Sweden (PCBaSe) 4.0 for a survival analysis. Among 199 620 men reported to have prostate cancer in 2000-2020, 172 112 were diagnosed on needle biopsy. The primary treatment was classified as androgen deprivation therapy (66%), deferred treatment (5%), radical prostatectomy (7%), or radical radiotherapy (21%). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The risks of death from prostate cancer in men with Gleason score 9-10 at 5 and 10 yr were used as endpoints. Multivariable Cox regression models controlling for socioeconomic factors and primary treatment were used for time-to-event analyses of death from prostate cancer and death from any causes. RESULTS AND LIMITATIONS: A total of 20 419 (12%) men had a Gleason score of 9-10, including Gleason scores of 4 + 5, 5 + 4, and 5 + 5 in 14 333 (70%), 4223 (21%), and 1863 (9%) men, respectively. The risks of prostate cancer death for men with Gleason scores 4 + 5, 5 + 4, and 5 + 5 at 10 yr of follow-up were 0.45 (confidence interval [CI] 0.44-0.46), 0.56 (0.55-0.58), and 0.66 (0.63-0.68), respectively. The risks of death of any cause for men with Gleason scores 4 + 5, 5 + 4, and 5 + 5 at 10 yr were 0.73 (CI 0.72-0.74), 0.81 (0.80-0.83), and 0.87 (0.85-0.89), respectively. CONCLUSIONS: We demonstrate in the largest and most complete cohort analyzed to date that collapsing the Gleason scores by grouping results in loss of prognostic information in men with Gleason score 9-10 cancer. PATIENT SUMMARY: Survival of prostate cancer patients with the highest tumor grades varies depending on grade composition.

A comprehensive pharmacist intervention to reduce morbidity in patients 80 years or older: a randomized controlled trial.

BACKGROUND: Patients 80 years or older are underrepresented in scientific studies. The objective of this study was to investigate the effectiveness of interventions performed by ward-based pharmacists in reducing morbidity and use of hospital care among older patients. METHODS: A randomized controlled study of patients 80 years or older was conducted at the University Hospital of Uppsala, Uppsala, Sweden. Four hundred patients were recruited consecutively between October 1, 2005, and June 30, 2006, and were randomized to control (n = 201) and intervention (n = 199) groups. The interventions were performed by ward-based pharmacists. The control group received standard care without direct involvement of pharmacists at the ward level. The primary outcome measure was the frequency of hospital visits (emergency department and readmissions [total and drug-related]) during the 12-month follow-up period. RESULTS: Three hundred sixty-eight patients (182 in the intervention group and 186 in the control group) were analyzed. For the intervention group, there was a 16% reduction in all visits to the hospital (quotient, 1.88 vs 2.24; estimate, 0.84; 95% confidence interval [CI], 0.72-0.99) and a 47% reduction in visits to the emergency department (quotient, 0.35 vs 0.66; estimate, 0.53; 95% CI, 0.37-0.75). Drug-related readmissions were reduced by 80% (quotient, 0.06 vs 0.32; estimate, 0.20; 95% CI, 0.10-0.41). After inclusion of the intervention costs, the total cost per patient in the intervention group was $230 lower than that in the control group. CONCLUSION: If implemented on a population basis, the addition of pharmacists to health care teams would lead to major reductions in morbidity and health care costs.

Correction for regression dilution bias using replicates from subjects with extreme first measurements.

The least squares estimator of the slope in a simple linear regression model will be biased towards zero when the predictor is measured with random error, i.e. intra-individual variation or technical measurement error. A correction factor can be estimated from a reliability study where one replicate is available on a subset of subjects from the main study. Previous work in this field has assumed that the reliability study constitutes a random subsample from the main study. We propose that a more efficient design is to collect replicates for subjects with extreme values on their first measurement. A variance formula for this estimator of the correction factor is presented. The variance for the corrected estimated regression coefficient for the extreme selection technique is also derived and compared with random subsampling. Results show that variances for corrected regression coefficients can be markedly reduced with extreme selection. The variance gain can be estimated from the main study data. The results are illustrated using Monte Carlo simulations and an application on the relation between insulin sensitivity and fasting insulin using data from the population-based ULSAM study. In conclusion, an investigator faced with the planning of a reliability study may wish to consider an extreme selection design in order to improve precision at a given number of subjects or alternatively decrease the number of subjects at a given precision.