Abstract Background Few interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF. Methods This was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF. Results Among 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81–10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04–1.44), and maximum intraoperative peak inspiratory pressure per cm H 2 0 (OR 1.14, 95% CI 1.06–1.22). Conclusions We identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation.
more »
« less
This content will become publicly available on September 14, 2024
Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches
Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance.
Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data.
2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features.
Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.
- NSF-PAR ID:
- 10492627
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Medicine
- Volume:
- 10
- ISSN:
- 2296-858X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
ABSTRACT BACKGROUND AND PURPOSE Functional magnetic resonance imaging (fMRI) is becoming widely recognized as a key component of preoperative neurosurgical planning, although intraoperative electrocortical stimulation (ECS) is considered the gold standard surgical brain mapping method. However, acquiring and interpreting ECS results can sometimes be challenging. This retrospective study assesses whether intraoperative availability of fMRI impacted surgical decision‐making when ECS was problematic or unobtainable.
METHODS Records were reviewed for 191 patients who underwent presurgical fMRI with fMRI loaded into the neuronavigation system. Four patients were excluded as a bur‐hole biopsy was performed. Imaging was acquired at 3 Tesla and analyzed using the general linear model with significantly activated pixels determined via individually determined thresholds. fMRI maps were displayed intraoperatively via commercial neuronavigation systems.
RESULTS Seventy‐one cases were planned ECS; however, 18 (25.35%) of these procedures were either not attempted or aborted/limited due to: seizure (10), patient difficulty cooperating with the ECS mapping (4), scarring/limited dural opening (3), or dural bleeding (1). In all aborted/limited ECS cases, the surgeon continued surgery using fMRI to guide surgical decision‐making. There was no significant difference in the incidence of postoperative deficits between cases with completed ECS and those with limited/aborted ECS.
CONCLUSIONS Preoperative fMRI allowed for continuation of surgery in over one‐fourth of patients in which planned ECS was incomplete or impossible, without a significantly different incidence of postoperative deficits compared to the patients with completed ECS. This demonstrates additional value of fMRI beyond presurgical planning, as fMRI data served as a backup method to ECS.
-
Gait speed assessment increases the predictive value of mortality and morbidity following older adults’ cardiac surgery. The purpose of this study was to improve clinical assessment and prediction of mortality and morbidity among older patients undergoing cardiac surgery through the identification of the relationships between preoperative gait and postural stability characteristics utilizing a noninvasive-wearable mobile phone device and postoperative cardiac surgical outcomes. This research was a prospective study of ambulatory patients aged over 70 years undergoing non-emergent cardiac surgery. Sixteen older adults with cardiovascular disease (Age 76.1 ± 3.6 years) scheduled for cardiac surgery within the next 24 h were recruited for this study. As per the Society of Thoracic Surgeons (STS) recommendation guidelines, eight of the cardiovascular disease (CVD) patients were classified as frail (prone to adverse outcomes with gait speed ≤0.833 m/s) and the remaining eight patients as non-frail (gait speed >0.833 m/s). Treating physicians and patients were blinded to gait and posture assessment results not to influence the decision to proceed with surgery or postoperative management. Follow-ups regarding patient outcomes were continued until patients were discharged or transferred from the hospital, at which time data regarding outcomes were extracted from the records. In the preoperative setting, patients performed the 5-m walk and stand still for 30 s in the clinic while wearing a mobile phone with a customized app “Lockhart Monitor” available at iOS App Store. Systematic evaluations of different gait and posture measures identified a subset of smartphone measures most sensitive to differences in two groups (frail versus non-frail) with adverse postoperative outcomes (morbidity/mortality). A regression model based on these smartphone measures tested positive on five CVD patients. Thus, clinical settings can readily utilize mobile technology, and the proposed regression model can predict adverse postoperative outcomes such as morbidity or mortality events.more » « less
-
Abstract Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.more » « less
-
more » « less
Abstract Background Psychological stress is prevalent among reproductive‐aged men. Assessment of semen quality for epidemiological studies is challenging as data collection is expensive and cumbersome, and studies evaluating the effect of perceived stress on semen quality are inconsistent.
Objective To examine the association between perceived stress and semen quality.
Material and methods We analyzed baseline data on 644 men (1,159 semen samples) from two prospective preconception cohort studies during 2015–2021: 592 in Pregnancy Study Online (PRESTO) and 52 in SnartForaeldre.dk (SF). At study entry, men aged ≥21 years (PRESTO) and ≥18 years (SF) trying to conceive without fertility treatment completed a questionnaire on reproductive and medical history, socio‐demographics, lifestyle, and the 10‐item version of the Perceived Stress Scale (PSS; interquartile range [IQR] of scores: 0–40). After enrollment (median weeks: 2.1, IQR: 1.3–3.7), men were invited to perform in‐home semen testing, twice with 7–10 days between tests, using the Trak Male Fertility Testing System. Semen quality was characterized by semen volume, sperm concentration, and total sperm count. We fit generalized estimating equation linear regression models to estimate the percent difference in mean log‐transformed semen parameters by four PSS groups (<10, 10–14, 15–19, ≥20), adjusting for potential confounders.
Results The median PSS score and IQR was 15 (10–19), and 136 men (21.1%) had a PSS score ≥20. Comparing men with PSS scores ≥20 with <10, the adjusted percent difference was −2.7 (95% CI: −9.8; 5.0) for semen volume, 6.8 (95% CI: ‐10.9; 28.1) for sperm concentration, and 4.3 (95% CI: −13.8; 26.2) for total sperm count.
Conclusion Our findings indicate that perceived stress is not materially associated with semen volume, sperm concentration, or total sperm count.
