We aimed to update a previously published, multi-institutional nomogram of outcomes for salvage radiotherapy (SRT) following radical prostatectomy (RP) for prostate cancer, including patients treated ...in the contemporary era.
Individual data from node-negative patients with a detectable post-RP prostate-specific antigen (PSA) treated with SRT with or without concurrent androgen-deprivation therapy (ADT) were obtained from 10 academic institutions. Freedom from biochemical failure (FFBF) and distant metastases (DM) rates were estimated, and predictive nomograms were generated.
Overall, 2,460 patients with a median follow-up of 5 years were included; 599 patients (24%) had a Gleason score (GS) ≤ 6, 1,387 (56%) had a GS of 7, 244 (10%) had a GS of 8, and 230 (9%) had a GS of 9 to 10. There were 1,370 patients (56%) with extraprostatic extension (EPE), 452 (18%) with seminal vesicle invasion (SVI), 1,434 (58%) with positive surgical margins, and 390 (16%) who received ADT (median, 6 months). The median pre-SRT PSA was 0.5 ng/mL (interquartile range, 0.3 to 1.1). The 5-yr FFBF rate was 56% overall, 71% for those with a pre-SRT PSA level of 0.01 to 0.2 ng/mL (n = 441), 63% for those with a PSA of 0.21 to 0.50 ng/mL (n = 822), 54% for those with a PSA of 0.51 to 1.0 ng/mL (n = 533), 43% for those with a PSA of 1.01 to 2.0 ng/mL (n = 341), and 37% for those with a PSA > 2.0 ng/mL (n = 323); P < .001. On multivariable analysis, pre-SRT PSA, GS, EPE, SVI, surgical margins, ADT use, and SRT dose were associated with FFBF. Pre-SRT PSA, GS, SVI, surgical margins, and ADT use were associated with DM, whereas EPE and SRT dose were not. The nomogram concordance indices were 0.68 (FFBF) and 0.74 (DM).
Early SRT at low PSA levels after RP is associated with improved FFBF and DM rates. Contemporary nomograms can estimate individual patient outcomes after SRT in the modern era.
IMPORTANCE: Although metabolic surgery (defined as procedures that influence metabolism by inducing weight loss and altering gastrointestinal physiology) significantly improves cardiometabolic risk ...factors, the effect on cardiovascular outcomes has been less well characterized. OBJECTIVE: To investigate the relationship between metabolic surgery and incident major adverse cardiovascular events (MACE) in patients with type 2 diabetes and obesity. DESIGN, SETTING, AND PARTICIPANTS: Of 287 438 adult patients with diabetes in the Cleveland Clinic Health System in the United States between 1998 and 2017, 2287 patients underwent metabolic surgery. In this retrospective cohort study, these patients were matched 1:5 to nonsurgical patients with diabetes and obesity (body mass index BMI ≥30), resulting in 11 435 control patients, with follow-up through December 2018. EXPOSURES: Metabolic gastrointestinal surgical procedures vs usual care for type 2 diabetes and obesity. MAIN OUTCOMES AND MEASURES: The primary outcome was the incidence of extended MACE (composite of 6 outcomes), defined as first occurrence of all-cause mortality, coronary artery events, cerebrovascular events, heart failure, nephropathy, and atrial fibrillation. Secondary end points included 3-component MACE (myocardial infarction, ischemic stroke, and mortality) and the 6 individual components of the primary end point. RESULTS: Among the 13 722 study participants, the distribution of baseline covariates was balanced between the surgical group and the nonsurgical group, including female sex (65.5% vs 64.2%), median age (52.5 vs 54.8 years), BMI (45.1 vs 42.6), and glycated hemoglobin level (7.1% vs 7.1%). The overall median follow-up duration was 3.9 years (interquartile range, 1.9-6.1 years). At the end of the study period, 385 patients in the surgical group and 3243 patients in the nonsurgical group experienced a primary end point (cumulative incidence at 8-years, 30.8% 95% CI, 27.6%-34.0% in the surgical group and 47.7% 95% CI, 46.1%-49.2% in the nonsurgical group P < .001; absolute 8-year risk difference ARD, 16.9% 95% CI, 13.1%-20.4%; adjusted hazard ratio HR, 0.61 95% CI, 0.55-0.69). All 7 prespecified secondary outcomes showed statistically significant differences in favor of metabolic surgery, including mortality. All-cause mortality occurred in 112 patients in the metabolic surgery group and 1111 patients in the nonsurgical group (cumulative incidence at 8 years, 10.0% 95% CI, 7.8%-12.2% and 17.8% 95% CI, 16.6%-19.0%; ARD, 7.8% 95% CI, 5.1%-10.2%; adjusted HR, 0.59 95% CI, 0.48-0.72). CONCLUSIONS AND RELEVANCE: Among patients with type 2 diabetes and obesity, metabolic surgery, compared with nonsurgical management, was associated with a significantly lower risk of incident MACE. The findings from this observational study must be confirmed in randomized clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03955952
Objective:Approximately 20%–35% of individuals 12–35 years old who meet criteria for a prodromal risk syndrome convert to psychosis within 2 years. However, this estimate ignores the fact that ...clinical high-risk cases vary considerably in risk. The authors sought to create a risk calculator, based on profiles of risk indicators, that can ascertain the probability of conversion to psychosis in individual patients.Method:The study subjects were 596 clinical high-risk participants from the second phase of the North American Prodrome Longitudinal Study who were followed up to the time of conversion to psychosis or last contact (up to 2 years). The predictors examined were limited to those that are supported by previous studies and are readily obtainable in general clinical settings. Time-to-event regression was used to build a multivariate model predicting conversion, with internal validation using 1,000 bootstrap resamples.Results:The 2-year probability of conversion to psychosis was 16%. Higher levels of unusual thought content and suspiciousness, greater decline in social functioning, lower verbal learning and memory performance, slower speed of processing, and younger age at baseline each contributed to individual risk for psychosis. Stressful life events, trauma, and family history of schizophrenia were not significant predictors. The multivariate model achieved a concordance index of 0.71 and, as reported in an article by Carrión et al., published concurrently with this one, was validated in an independent external data set. The results are instantiated in a web-based risk prediction tool envisioned to be most useful in research protocols involving the psychosis prodrome.Conclusions:A risk calculator comparable in accuracy to those for cardiovascular disease and cancer is available to predict individualized conversion risks in newly ascertained clinical high-risk cases. Given that the risk calculator can be validly applied only for patients who screen positive on the Structured Clinical Interview for Psychosis Risk Syndromes, which requires training to administer, its most immediate uses will be in research on psychosis risk factors and in research-driven clinical (prevention) trials.
Aim
Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining ...electronic health records (EHR) and biomarkers.
Methods
This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values PPV/NPV, and net reclassification index NRI) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years.
Results
In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min
−1
1.73 m
−2
, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (
n
= 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (
n
= 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (
p
< 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRI
event
for the high-risk group was 41% (
p
< 0.05).
Conclusions
KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.
Graphical abstract
Abstract Background Prostate tumor heterogeneity and biopsy undersampling pose challenges to accurate, individualized risk assessment for men with localized disease. Objective To identify and ...validate a biopsy-based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death, and adverse pathology. Design, setting, and participants Gene expression was quantified by reverse transcription–polymerase chain reaction for three studies—a discovery prostatectomy study ( n = 441), a biopsy study ( n = 167), and a prospectively designed, independent clinical validation study ( n = 395)—testing retrospectively collected needle biopsies from contemporary (1997–2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS). Outcome measures and statistical analysis The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility. Results and limitations Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio OR per 20 GPS units: 2.3; 95% confidence interval CI, 1.5–3.7; p < 0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1.3–3.0; p = 0.003) at surgical pathology. GPS predicted high-grade and/or high-stage disease after controlling for established clinical factors ( p < 0.005) such as an OR of 2.1 (95% CI, 1.4–3.2) when adjusting for Cancer of the Prostate Risk Assessment score. A limitation of the validation study was the inclusion of men with low-volume intermediate-risk PCa (Gleason score 3 + 4), for whom some providers would not consider AS. Conclusions Genes representing multiple biological pathways discriminate PCa aggressiveness in biopsy tissue despite tumor heterogeneity, multifocality, and limited sampling at time of biopsy. The biopsy-based 17-gene GPS improves prediction of the presence or absence of adverse pathology and may help men with PCa make more informed decisions between AS and immediate treatment. Patient summary Prostate cancer (PCa) is often present in multiple locations within the prostate and has variable characteristics. We identified genes with expression associated with aggressive PCa to develop a biopsy-based, multigene signature, the Genomic Prostate Score (GPS). GPS was validated for its ability to predict men who have high-grade or high-stage PCa at diagnosis and may help men diagnosed with PCa decide between active surveillance and immediate definitive treatment.
The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model ...performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic ROC curve), and goodness-of-fit statistics for calibration. Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision—analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions. We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation). We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
Purpose Long-term prostate cancer specific mortality after radical prostatectomy is poorly defined in the era of widespread screening. An understanding of the treated natural history of screen ...detected cancers and the pathological risk factors for prostate cancer specific mortality are needed for treatment decision making. Materials and Methods Using Fine and Gray competing risk regression analysis we modeled clinical and pathological data, and followup information on 11,521 patients treated with radical prostatectomy at a total of 4 academic centers from 1987 to 2005 to predict prostate cancer specific mortality. The model was validated on 12,389 patients treated at a separate institution during the same period. Median followup in the modeling and validation cohorts was 56 and 96 months, respectively. Results The overall 15-year prostate cancer specific mortality rate was 7%. Primary and secondary Gleason grade 4–5 (each p <0.001), seminal vesicle invasion (p <0.001) and surgery year (p = 0.002) were significant predictors of prostate cancer specific mortality. A nomogram predicting 15-year prostate cancer specific mortality based on standard pathological parameters was accurate and discriminating with an externally validated concordance index of 0.92. When stratified by patient age at diagnosis, the 15-year prostate cancer specific mortality rate for pathological Gleason score 6 or less, 3 + 4, 4 + 3 and 8–10 was 0.2% to 1.2%, 4.2% to 6.5%, 6.6% to 11% and 26% to 37%, respectively. The 15-year prostate cancer specific mortality risk was 0.8% to 1.5%, 2.9% to 10%, 15% to 27% and 22% to 30% for organ confined cancer, extraprostatic extension, seminal vesicle invasion and lymph node metastasis, respectively. Only 3 of 9,557 patients with organ confined, pathological Gleason score 6 or less cancer died of prostate cancer. Conclusions Poorly differentiated cancer and seminal vesicle invasion are the prime determinants of prostate cancer specific mortality after radical prostatectomy. The prostate cancer specific mortality risk can be predicted with remarkable accuracy after the pathological features of prostate cancer are known.
OBJECTIVESurgery is an effective but costly treatment for many patients with drug-resistant temporal lobe epilepsy (DR-TLE). We aim to evaluate whether, in the United States, surgery is ...cost-effective compared to medical management for patients deemed surgical candidates and whether surgical evaluation is cost-effective for patients with DR-TLE in general.
METHODSWe use a semi-Markov model to assess the cost-effectiveness of surgery and surgical evaluation over a lifetime horizon. We use second-order Monte Carlo simulations to conduct probabilistic sensitivity analyses to estimate variation in model output. We adopt both health care and societal perspectives, including direct health care costs (e.g., surgery, antiepileptic drugs) and indirect costs (e.g., lost earnings by patients and care providers.) We compare the incremental cost-effectiveness ratio to societal willingness to pay (∼$100,000 per quality-adjusted life-year QALY) to determine whether surgery is cost-effective.
RESULTSEpilepsy surgery is cost-effective compared to medical management in surgically eligible patients by virtue of being cost-saving ($328,000 vs $423,000) and more effective (16.6 vs 13.6 QALY) than medical management in the long run. Surgical evaluation is cost-effective in patients with DR-TLE even if the probability of being deemed a surgical candidate is only 5%. From a societal perspective, surgery becomes cost-effective within 3 years, and 89% of simulations favor surgery over the lifetime horizon.
CONCLUSIONFor surgically eligible patients with DR-TLE, surgery is cost-effective. For patients with DR-TLE in general, referral for surgical evaluation (and possible subsequent surgery) is cost-effective. Patients with DR-TLE should be referred for surgical evaluation without hesitation on cost-effectiveness grounds.
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