Meier, Jens

Rationale Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. Methods In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). Results A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72–0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64–0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59–0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65–0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61–0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62–0.69; p=0.87). Conclusions In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. Trial registration number NCT03498352 , NCT04826575 .

Background: Perioperative anemia has been associated with increased risk of red blood cell transfusion and increased morbidity and mortality after surgery. The optimal approach to the diagnosis and management of perioperative anemia is not fully established. Objective: To develop consensus recommendations for anemia management in surgical patients. Methods: An international expert panel reviewed the current evidence and developed recommendations using modified RAND Delphi methodology. Results: The panel recommends that all patients except those undergoing minor procedures be screened for anemia before surgery. Appropriate therapy for anemia should be guided by an accurate diagnosis of the etiology. The need to proceed with surgery in some patients with anemia is expected to persist. However, early identification and effective treatment of anemia has the potential to reduce the risks associated with surgery and improve clinical outcomes. As with preoperative anemia, postoperative anemia should be treated in the perioperative period. Conclusions: Early identification and effective treatment of anemia has the potential to improve clinical outcomes in surgical patients.

The World Health Organization (WHO) has declared coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemic. Global health care now faces unprecedented challenges with widespread and rapid human-to-human transmission of SARS-CoV-2 and high morbidity and mortality with COVID-19 worldwide. Across the world, medical care is hampered by a critical shortage of not only hand sanitizers, personal protective equipment, ventilators, and hospital beds, but also impediments to the blood supply. Blood donation centers in many areas around the globe have mostly closed. Donors, practicing social distancing, some either with illness or undergoing self-quarantine, are quickly diminishing. Drastic public health initiatives have focused on containment and “flattening the curve” while invaluable resources are being depleted. In some countries, the point has been reached at which the demand for such resources, including donor blood, outstrips the supply. Questions as to the safety of blood persist. Although it does not appear very likely that the virus can be transmitted through allogeneic blood transfusion, this still remains to be fully determined. As options dwindle, we must enact regional and national shortage plans worldwide and more vitally disseminate the knowledge of and immediately implement patient blood management (PBM). PBM is an evidence-based bundle of care to optimize medical and surgical patient outcomes by clinically managing and preserving a patient’s own blood. This multinational and diverse group of authors issue this “Call to Action” underscoring “The Essential Role of Patient Blood Management in the Management of Pandemics” and urging all stakeholders and providers to implement the practical and commonsense principles of PBM and its multiprofessional and multimodality approaches.

Objectives: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission. Design: Post hoc analysis of a single-center, prospective, before-and-after feasibility trial. Setting: Rural district hospital in Rwanda, a low-income country in Sub-Sahara Africa. Patients: Infants and children greater than 28 days and less than 18 years of life hospitalized because of an acute infection. Interventions: None. Measurements and Main Results: Age, vital signs (heart rate, respiratory rate, and temperature) capillary refill time, altered mental state collected at hospital admission, as well as survival status at hospital discharge were extracted from the trial database. This information was collected for 1,579 adult and pediatric patients admitted to a regional referral hospital with an acute infection in rural Rwanda. Nine-hundred forty-nine children were included in this analysis. We predicted survival in study subjects using random forests, a machine learning algorithm. Five prediction models, all including age plus two to five other variables, were tested. Three distinct optimization criteria of the algorithm were then compared. The in-hospital mortality was 1.5% (n = 14). All five models could predict in-hospital mortality with an area under the receiver operating characteristic curve ranging between 0.69 and 0.8. The model including age, respiratory rate, capillary refill time, altered mental state exhibited the highest predictive value area under the receiver operating characteristic curve 0.8 (95% CI, 0.78–0.8) with the lowest possible number of variables. Conclusions: A machine learning-based algorithm could reliably predict hospital mortality in a Sub-Sahara African population of 949 children with an acute infection using easily collected information at admission which includes age, respiratory rate, capillary refill time, and altered mental state. Future studies need to evaluate and strengthen this algorithm in larger pediatric populations, both in high- and low-/middle-income countries.