Context Intraoperative hemodynamic instability (HI) deteriorates surgical outcomes of patients with normotensive pheochromocytoma (NP). Objective To characterize the hemodynamics of NP and develop and externally validate a prediction model for intraoperative HI. Design, Setting and Patients Data on 117 patients with NP (derivation cohort) and 40 patients with normotensive adrenal myelolipoma (NAM), who underwent laparoscopic adrenalectomy from January 2011 to November 2021, were retrospectively collected. Data on 22 patients with NP (independent validation cohort) were collected from another hospital during the same period. Main Outcome Measures The hemodynamic characteristics of patients with NP and NAM were compared. Machine learning models were used to identify risk factors associated with HI. The final model was visualized via nomogram. Results Forty-eight (41%) out of 117 patients experienced HI, which was significantly more than that for NAM. A multivariate logistic regression including age, tumor size, fasting plasma glucose, and preoperative systolic blood pressure showed good discrimination measured by area under curve (0.8286; 95% CI, 0.6875–0.9696 and 0.7667; 95% CI, 0.5386–0.9947) for predicting HI in internal and independent validation cohorts, respectively. The sensitivities and positive predictive values were 0.6667 and 0.7692 for the internal and 0.9167 and 0.6111 for the independent validations, respectively. The final model was visualized via nomogram and yielded net benefits across a wide range of risk thresholds in decision curve analysis. Conclusions Patients with normotensive pheochromocytoma experienced HI during laparoscopic adrenalectomy. The nomogram can be used for individualized prediction of intraoperative HI in patients with NP.

Purpose Pediatric patients with inborn errors of immunity (IEI) undergoing umbilical cord blood transplantation (UCBT) are at risk of early mortality. Our aim was to develop and validate a prediction model for early mortality after UCBT in pediatric IEI patients based on pretransplant factors. Methods Data from 230 pediatric IEI patients who received their first UCBT between 2014 and 2021 at a single center were analyzed retrospectively. Data from 2014–2019 and 2020–2021 were used as training and validation sets, respectively. The primary outcome of interest was early mortality. Machine learning algorithms were used to identify risk factors associated with early mortality and to build predictive models. The model with the best performance was visualized using a nomogram. Discriminative ability was measured using the area under the curve (AUC) and decision curve analysis. Results Fifty days was determined as the cutoff for distinguishing early mortality in pediatric IEI patients undergoing UCBT. Of the 230 patients, 43 (18.7%) suffered early mortality. Multivariate logistic regression with pretransplant albumin, CD4 (absolute count), elevated C-reactive protein, and medical history of sepsis showed good discriminant AUC values of 0.7385 (95% CI, 0.5824–0.8945) and 0.827 (95% CI, 0.7409–0.9132) in predicting early mortality in the validation and training sets, respectively. The sensitivity and specificity were 0.5385 and 0.8154 for validation and 0.7667 and 0.7705 for training, respectively. The final model yielded net benefits across a reasonable range of risk thresholds. Conclusion The developed nomogram can predict early mortality in pediatric IEI patients undergoing UCBT.