Dataset Information

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

ABSTRACT:

Background and objectives

Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions.

Methods

A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission.

Results

An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88).

Conclusions

The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.

SUBMITTER: Mestrom EHJ

PROVIDER: S-EPMC10399824 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

Mestrom Eveline H J EHJ Bakkes Tom H G F THGF Ourahou Nassim N Korsten Hendrikus H M HHM Serra Paulo de Andrade PA Montenij Leon J LJ Mischi Massimo M Turco Simona S Bouwman R Arthur RA

PloS one 20230803 8

<h4>Background and objectives</h4>Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU ...[more]

PMID: 37535542

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Project description:ObjectiveThis paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes.Materials and methodsWith less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.ResultsDuring evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.DiscussionThe study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.ConclusionsThis work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Project description:IntroductionPosterior fossa decompression for Chiari I Malformation is a common pediatric neurosurgical procedure. We sought to identify the impact of anesthesia-related intraoperative complications on unanticipated admission to the intensive care unit and outcomes following posterior fossa decompression.MethodsMedical records of all patients <18 years who underwent surgery for Chiari I malformation between 1/1/09 and 1/31/21 at the Ann & Robert H. Lurie Children's Hospital of Chicago were included. Records were reviewed for patient characteristics, anesthesia-related intraoperative complications, postoperative complications, and surgical outcomes. The primary outcome was the incidence of unanticipated admission to the intensive care unit, and the primary variable of interest was an anesthesia-related intraoperative complication. Patient, surgical characteristics, and year of surgery were also compared between patients with and without an unanticipated admission to the intensive care unit, and a multi-variable adjusted estimate of odds of unanticipated admission to the intensive care unit admission following an anesthesia-related intraoperative complication was performed. Secondary outcomes included anesthesia factors associated with an anesthesia-related intraoperative event, and postoperative complications and surgical outcomes between patients admitted to the intensive care unit and those who were not.ResultsTwo hundred ninety-six patients with Chiari I Malformation were identified. Clinical characteristics associated with an unanticipated admission to the intensive care unit were younger age, American Society of Anesthesiologist (ASA) physical status >2 and an anesthesia-related intraoperative complication. 29 anesthesia-related intraoperative complications were observed in 25 patients (8.4%). Two of 25 patients (8%) with an anesthesia-related intraoperative complication compared with 3 of 271 (1%) patients without anesthesia-related intraoperative complication had an unanticipated admission to the intensive care unit, odds ratio 7.8 (95% CI 1.2-48.8, p = .010). When adjusted for age, sex, ASA physical status, presenting symptoms, concomitant syringomyelia, previous decompression surgery and year of surgery, the odds ratio for an unanticipated admission to the intensive care unit following an anesthesia-related intraoperative complication was 5.9 (95% CI 0.51-59.6, p = .149). There were no differences in surgical outcomes between patients with or without an unanticipated admission to the intensive care unit.ConclusionOur study demonstrates that although anesthesia-related intraoperative complications during posterior fossa decompression are infrequent, they are associated with an increased risk of an unanticipated admission to the intensive care unit.

Project description:ObjectiveTo compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.BackgroundPrediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.MethodsAll patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).ResultsA total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively.ConclusionsMachine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.

Project description:BackgroundMultiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality.MethodsIn this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05).ResultsIn 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84-0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68-0.69) versus 0.67 (95% CI 0.67-0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51-0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53-0.55) for patients with a LOS of at least 7 days.ConclusionsComparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

Project description:BackgroundEffectively identifying deteriorated patients is vital to the development and validation of automated systems designed to predict clinical deterioration. Existing outcome measures used for this purpose have significant limitations. Published criteria for admission to high acuity inpatient areas may represent markers of patient deterioration and could inform the development of alternate outcome measures.ObjectivesIn this scoping review, we aimed to characterise published criteria for admission of adult inpatients to high acuity inpatient areas including intensive care units. A secondary aim was to identify variables that are extractable from electronic health records (EHRs).Data sourcesElectronic databases PubMed and ProQuest EBook Central were searched to identify papers published from 1999 to date of search. We included publications which described prescriptive criteria for admission of adult inpatients to a clinical area with a higher level of care than a general hospital ward.Charting methodsData was extracted from each publication using a standardised data-charting form. Admission criteria characteristics were summarised and cross-tabulated for each criterion by population group.ResultsFive domains were identified: diagnosis-based criteria, clinical parameter criteria, organ-support criteria, organ-monitoring criteria and patient baseline criteria. Six clinical parameter-based criteria and five needs-based criteria were frequently proposed and represent variables extractable from EHRs. Thresholds for objective clinical parameter criteria varied across publications, and by disease subgroup, and universal cut-offs for criteria could not be elucidated.ConclusionsThis study identified multiple criteria which may represent markers of deterioration. Many of the criteria are extractable from the EHR, making them potential candidates for future automated systems. Variability in admission criteria and associated thresholds across the literature suggests clinical deterioration is a heterogeneous phenomenon which may resist being defined as a single entity via a consensus-driven process.

Dataset Information

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

Background and objectives

Methods

Results

Conclusions

Publications

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

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