Predicting Postoperative Physiologic Decline After Surgery

Abstract

BACKGROUND: Projecting postoperative recovery in pediatric surgical patients is challenging. We assessed how the patients' number of complex chronic conditions (CCCs) and chronic medications interacted with active health issues to influence the likelihood of postoperative physiologic decline (PoPD). METHODS: A prospective study of 3295 patients undergoing elective surgery at a freestanding children's hospital. During preoperative clinical evaluation, active health problems, CCCs, and medications were documented. PoPD (compromise of cardiovascular, respiratory, and/or neurologic systems) was measured prospectively every 4 hours by inpatient nurses. PoPD odds were estimated with multivariable logistic regression. Classification and regression tree analysis distinguished children with the highest and lowest likelihood of PoPD. RESULTS: Median age at surgery was 8 years (interquartile range: 2-15); 2336 (70.9%) patients had a CCC; and 241 (7.3%) used $11 home medications. During preoperative evaluation, 1556 (47.2%) patients had $1 active health problem. After surgery, 882 (26.8%) experienced PoPD. The adjusted odds of PoPD were 1.2 (95% confidence interval [CI]: 1.0-1.4) for presence versus absence of an active health problem; 1.4 (95% CI: 1.0-1.9) for $11 vs 0 home medications; and 2.2 (95% CI: 1.7-2.9) for $3 vs 0 CCCs. In classification and regression tree analysis, the lowest rate of PoPD (8.6%) occurred in children without an active health problem at the preoperative evaluation; the highest rate (57.2%) occurred in children with a CCC who used $11 home medications. CONCLUSIONS: Greater than 1 in 4 pediatric patients undergoing elective surgery experienced PoPD. Combinations of active health problems at preoperative evaluation, polypharmacy, and multimorbidity distinguished patients with a low versus high risk of PoPD.

Hospital resource use after hip reconstruction surgery in children with neurological complex chronic conditions

“…11 Co-occurring conditions are also emerging as one of the most influential predictors of postoperative physiological decline after all-condition elective surgeries in children. 10 Unfortunately, there is limited scientific evidence on the most effective ways to assess and manage the severity of the children's co-occurring conditions ahead of major hip and spine surgeries to minimize their impact on postoperative recovery.…”

“…Earlier studies report that co-occurring conditions in children are associated with higher rates of postoperative physiological decline and increased hospital resource use. 10,11 Preoperative assessment and control of co-occurring conditions in children undergoing major operations, including hip reconstruction, is essential to minimize complications during postoperative recovery. [12][13][14] The current study was conducted to advance knowledge on the relationship between co-occurring conditions and postoperative outcomes in children with neurological CCCs undergoing hip reconstruction surgery for hip subluxation.…”

“…The underlying neurological disease mediates these observed impairments. Earlier studies report that co‐occurring conditions in children are associated with higher rates of postoperative physiological decline and increased hospital resource use 10,11 . Preoperative assessment and control of co‐occurring conditions in children undergoing major operations, including hip reconstruction, is essential to minimize complications during postoperative recovery 12–14 …”

Developing an Assessment Tool for Post-Surgical Paediatric Rehabilitative Care in Tanzania: an Interprofessional Approach

“…Currently, there are few examples in the literature on outcome measurement tools for post-surgical paediatric care in low-resource settings. The literature focuses primarily on client safety outcomes or quality of care centred around the time in the hospital (Butler, 2016;Lelli Chiesa et al, 2020;Philipo et al, 2020) or preparing for surgery (de Oliveira Pires et al, 2013), thus overlooking postoperative rehabilitation (Berry et al, 2019). It is noteworthy that there are diligent efforts to address client safety by the WHO in creating Global Patient Safety Action Plan 2021-2030, with a vision of prioritising safe and respectful care (World Health Organisation, 2021).…”

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

“…They implemented a logistic regression model with 155 variables extracted from 36 measurements including vital signs, to predict ICU transfer for children in acute wards within the first 24 h of hospital admission, which showed an 0.91 area under the receiver operating characteristic (AUROC) in the test set. Then, several other ML-based models were proposed to predict clinical deterioration and/or an unplanned ward-to-ICU transfer ( 13 , 14 , 17 , 20 , 22 , 26 ), adverse events within the ICU ( 15 , 18 , 19 , 21 , 23 , 24 , 27 ) or need for critical care within the emergency department ( 28 ). However, the best results were achieved when vital signs data were combined with other variables such as medications and/or laboratory test data as shown by Ruiz et al ( 15 ).…”