Transperineal template saturation and conventional biopsy for stage prediction in prostate cancer

Objective

To evaluate the performance of risk calculators (RCs) predicting lymph node invasion (LNI) and extraprostatic extension (EPE) in men undergoing transperineal magnetic resonance imaging/transrectal ultrasound (TRUS)-fusion template saturation biopsy (TTSB) and conventional systematic TRUS-guided biopsy (SB).

Patients and Methods

The RCs were tested in a consecutive cohort of 645 men undergoing radical prostatectomy with extended pelvic LN dissection between 2005 and 2019. TTSB was performed in 230 (35.7%) and SB in 415 (64.3%) men. Risk of LNI and EPE was calculated using the available RCs. Discrimination, calibration, and clinical usefulness stratified by different biopsy techniques were assessed.

Results

Lymph node invasion was observed in 23 (10%) and EPE in 73 (31.8%) of cases with TTSB and 53 (12.8%) and 158 (38%) with SB, respectively. RCs showed an excellent discrimination and acceptable calibration for prediction of LNI based on TTSB (area under the curve [AUC]/risk estimation: Memorial Sloan Kettering Cancer Center [MSKCC]-RC 0.79/−4%, Briganti (2012)-RC 0.82/−4%, Gandaglia-RC 0.81/+6%). These were comparable in SB (MSKCC-RC 0.78/+2%; Briganti (2012)-RC 0.77/−3%). Decision curve analysis (DCA) revealed a net benefit at threshold probabilities between 3% and 6% when TTSB was used. For prediction of EPE based on TTSB an inferior discrimination and variable calibration were observed (AUC/risk estimation: MSKCC-RC 0.71/+8% and Martini (2018)-RC 0.69/+2%) achieving a net benefit on DCA only at risk thresholds of >17%. Performance of RCs for prediction of LNI and EPE based on SB showed comparable results with a better performance in the DCA for LNI (risk thresholds 1–2%) and poorer performance for EPE (risk threshold >20%). This study is limited by its retrospective single-institution design.

Conclusions

The potentially more accurate grading ability of TTSB did not result in improved performance of preoperative RCs. Prediction tools for LNI proved clinical usefulness while RCs for EPE did not.     

Development and validation of a prediction model for outcomes after transaxillary first rib resection for neurogenic thoracic outlet syndrome following strict Society for Vascular Surgery diagnostic criteria

ObjectiveNeurogenic thoracic outlet syndrome (NTOS) is the most common form of thoracic outlet syndrome. However, NTOS has remained difficult to diagnose and treat successfully. The purpose of the present study was to generate a predictive clinical calculator for postoperative outcomes after first rib resection (FRR) for NTOS.MethodsWe performed a retrospective review of patients who had undergone FRR for NTOS at a single tertiary care institution between 2016 and 2020. A multivariate stepwise logistic regression analysis was performed to assess the association of the percentage of improvement after FRR with the patient baseline characteristics, pertinent clinical characteristics, and diagnostic criteria set by the Society for Vascular Surgery. The primary outcome was subjective patient improvement after FRR. A prediction risk calculator was developed using backward stepwise multivariate logistic regression coefficients. Bootstrapping was used for internal validation.ResultsA total of 208 patients (22.2% male; mean age, 35.8 ± 12.8 years; median follow-up, 44.9 months) had undergone 243 FRRs. Of the 208 patients, 94.7% had had symptoms localized to the supraclavicular area, and 97.6% had had symptoms in the hand. All the patients had had positive symptoms reproduced by the elevated arm stress test and upper limb tension test. Another reasonably likely diagnosis was absent for all the patients. Of the 196 patients who had received a lidocaine injection, 180 (93.3%) had experienced improvement of NTOS symptoms. Of the 95 patients who had received a Botox injection, 82 (74.6%) had experienced improvement of NTOS symptoms. Receiver operating characteristic curve analysis was used to assess the model. The area under the curve for the backward stepwise multivariate logistic regression model was 0.8. The multivariate logistic regression analyses revealed that the significant predictors of worsened clinical outcomes included hand weakness (adjusted odds ratio [aOR], 4.28; 95% confidence interval [CI], 1.04-17.74), increasing age (aOR, 0.93; 95% CI, 0.88-0.99), workers’ compensation or litigation case (aOR, 0.09; 95% CI, 0.01-0.82), and symptoms in the dominant hand (aOR, 0.20; 95% CI, 0.05-0.88).ConclusionsUsing retrospective data from a single-institution database, we have developed a prediction calculator with moderate to high predictive ability, as demonstrated by an area under the curve of 0.8. The tool (available at: https://jhhntosriskcalculator.shinyapps.io/NTOS_calc/) is an important adjunct to clinical decision-making that can offer patients and providers realistic and personalized expectations of the postoperative outcome after FRR for NTOS. The findings from the present study have reinforced the diagnostic criteria set by the Society for Vascular Surgery. The calculator could aid physicians in surgical planning, referrals, and counseling patients on whether to proceed with surgery.     

Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach

Background and Aim

Data are lacking on predicting inpatient mortality (IM) in patients admitted for inflammatory bowel disease (IBD). IM is a critical outcome; however, difficulty in its prediction exists due to infrequent occurrence. We assessed IM predictors and developed a predictive model for IM using machine-learning (ML).

Methods

Using the National Inpatient Sample (NIS) database (2005–2017), we extracted adults admitted for IBD. After ML-guided predictor selection, we trained and internally validated multiple algorithms, targeting minimum sensitivity and positive likelihood ratio (+LR) ≥ 80% and ≥ 3, respectively. Diagnostic odds ratio (DOR) compared algorithm performance. The best performing algorithm was additionally trained and validated for an IBD-related surgery sub-cohort. External validation was done using NIS 2018.

Results

In 398 426 adult IBD admissions, IM was 0.32% overall, and 0.87% among the surgical cohort (n = 40 784). Increasing age, ulcerative colitis, IBD-related surgery, pneumonia, chronic lung disease, acute kidney injury, malnutrition, frailty, heart failure, blood transfusion, sepsis/septic shock and thromboembolism were associated with increased IM. The QLattice algorithm, provided the highest performance model (+LR: 3.2, 95% CI 3.0–3.3; area-under-curve [AUC]:0.87, 85% sensitivity, 73% specificity), distinguishing IM patients by 15.6-fold when comparing high to low-risk patients. The surgical cohort model (+LR: 8.5, AUC: 0.94, 85% sensitivity, 90% specificity), distinguished IM patients by 49-fold. Both models performed excellently in external validation. An online calculator ( https://clinicalc.ai/im-ibd/ ) was developed allowing bedside model predictions.

Conclusions

An online prediction-model calculator captured > 80% IM cases during IBD-related admissions, with high discriminatory effectiveness. This allows for risk stratification and provides a basis for assessing interventions to reduce mortality in high-risk patients.     

Evaluation of a modified emergency surgical acuity score in predicting operative and non-operative mortality and morbidity in an acute surgical unit

Background

Emergency general surgery (EGS) patients have an increased risk of mortality and morbidity compared to other surgical patients. Limited risk assessment tools exist for use in both operative and non-operative EGS patients. We assessed the accuracy of a modified Emergency Surgical Acuity Score (mESAS) in EGS patients at our institution.

Methods

A retrospective cohort study from an acute surgical unit at a tertiary referral hospital was performed. Primary endpoints assessed included death before discharge, length of stay (LOS) >5 days and unplanned readmission within 28 days. Operative and non-operative patients were analysed separately. Validation was performed using the area under the receiver operating characteristic (AUROC), Brier score and Hosmer-Lemeshow test.

Results

A total of 1763 admissions between March 2018 and June 2021 were included for analysis. The mESAS was an accurate predictor of both death before discharge (AUROC 0.979, Brier score 0.007, Hosmer–Lemeshow P = 0.981) and LOS >5 days (0.787, 0.104, and 0.253, respectively). The mESAS was less accurate in predicting readmission within 28 days (0.639, 0.040, and 0.887, respectively). The mESAS retained its predictive ability for death before discharge and LOS >5 days in the split cohort analysis.

Conclusion

This study is the first to validate a modified ESAS in a non-operatively managed EGS population internationally and the first to validate the mESAS in Australia. The mESAS accurately predicts death before discharge and prolonged LOS for all EGS patients, providing a highly useful tool for surgeons and EGS units worldwide.