Development of a prediction rule for estimating postoperative pulmonary complications.
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ABSTRACT:
SUBMITTER: Jeong BH
PROVIDER: S-EPMC4249954 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Development of a prediction rule for estimating postoperative pulmonary complications.
Jeong Byeong-Ho BH Shin Beomsu B Eom Jung Seop JS Yoo Hongseok H Song Wonjun W Han Sangbin S Lee Kyung Jong KJ Jeon Kyeongman K Um Sang-Won SW Koh Won-Jung WJ Suh Gee Young GY Chung Man Pyo MP Kim Hojoong H Kwon O Jung OJ Woo Sookyoung S Park Hye Yun HY
PloS one 20141201 12
Patient- and procedure-related factors associated with postoperative pulmonary complications (PPCs) have changed over the last decade. Therefore, we sought to identify independent risk factors of PPCs and to develop a clinically applicable scoring system. We retrospectively analyzed clinical data from 2,059 patients who received preoperative evaluations from respiratory physicians between June 2011 and October 2012. A new scoring system for estimating PPCs was developed using beta coefficients o ...[more]
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Project description:Postoperative pulmonary complications (PPCs) are significant causes of postoperative morbidity and mortality. This study presents the utilization of machine learning for predicting PPCs and aims to identify the important features of the prediction models. This study used a retrospective cohort design and collected data from two hospitals. The dataset included perioperative variables such as patient characteristics, preexisting diseases, and intraoperative factors. Various algorithms, including logistic regression, random forest, light-gradient boosting machines, extreme-gradient boosting machines, and multilayer perceptrons, have been employed for model development and evaluation. This study enrolled 111,212 adult patients, with an overall incidence rate of 8.6% for developing PPCs. The area under the receiver-operating characteristic curve (AUROC) of the models was 0.699-0.767, and the f1 score was 0.446-0.526. In the prediction models, except for multilayer perceptron, the 10 most important features were obtained. In feature-reduced models, including 10 important features, the AUROC was 0.627-0.749, and the f1 score was 0.365-0.485. The number of packed red cells, urine, and rocuronium doses were similar in the three models. In conclusion, machine learning provides valuable insights into PPC prediction, significant features for prediction, and the feasibility of models that reduce the number of features.
| S-EPMC10488713 | biostudies-literature
Project description:BackgroundCurrently used pre-operative prediction scores for postoperative pulmonary complications (PPCs) use patient data and expected surgery characteristics exclusively. However, intra-operative events are also associated with the development of PPCs.ObjectiveWe aimed to develop a new prediction score for PPCs that uses both pre-operative and intra-operative data.DesignThis is a secondary analysis of the LAS VEGAS study, a large international, multicentre, prospective study.SettingsA total of 146 hospitals across 29 countries.PatientsAdult patients requiring intra-operative ventilation during general anaesthesia for surgery.InterventionsThe cohort was randomly divided into a development subsample to construct a predictive model, and a subsample for validation.Main outcome measuresPrediction performance of developed models for PPCs.ResultsOf the 6063 patients analysed, 10.9% developed at least one PPC. Regression modelling identified 13 independent risk factors for PPCs: six patient characteristics [higher age, higher American Society of Anesthesiology (ASA) physical score, pre-operative anaemia, pre-operative lower SpO2 and a history of active cancer or obstructive sleep apnoea], two procedure-related features (urgent or emergency surgery and surgery lasting ≥ 1 h), and five intra-operative events [use of an airway other than a supraglottic device, the use of intravenous anaesthetic agents along with volatile agents (balanced anaesthesia), intra-operative desaturation, higher levels of positive end-expiratory pressures > 3 cmH2O and use of vasopressors]. The area under the receiver operating characteristic curve of the LAS VEGAS risk score for prediction of PPCs was 0.78 [95% confidence interval (95% CI), 0.76 to 0.80] for the development subsample and 0.72 (95% CI, 0.69 to 0.76) for the validation subsample.ConclusionThe LAS VEGAS risk score including 13 peri-operative characteristics has a moderate discriminative ability for prediction of PPCs. External validation is needed before use in clinical practice.Trial registrationThe study was registered at Clinicaltrials.gov, number NCT01601223.
| S-EPMC7450515 | biostudies-literature
Project description:BackgroundPostoperative pulmonary complication (PPC) is a leading cause of mortality and poor outcomes in postoperative patients. No studies have enrolled intensive care unit (ICU) patients after noncardiac thoracic surgery, and effective prediction models for PPC have not been developed. This study aimed to explore the incidence and risk factors and construct prediction models for PPC in these patients.MethodsThis study retrospectively recruited patients admitted to the ICU after noncardiac thoracic surgery at West China Hospital, Sichuan University, from July 2019 to December 2022. The patients were randomly divided into a development cohort and a validation cohort at a 70% versus 30% ratio. The preoperative, intraoperative and postoperative variables during the ICU stay were compared. Univariate and multivariate logistic regression analyses were applied to identify candidate predictors, establish prediction models, and compare the accuracy of the models with that of reported risk models.ResultsA total of 475 ICU patients were enrolled after noncardiac thoracic surgery (median age, 58; 72% male). At least one PPC occurred in 171 patients (36.0%), and the most common PPC was pneumonia (153/475, 32.21%). PPC significantly increased the duration of mechanical ventilation (p < 0.001), length of ICU stay (p < 0.001), length of hospital stay (LOS) (p < 0.001), and rate of reintubation (p = 0.047) in ICU patients. Seven risk factors were identified, and then the prediction nomograms for PPC were constructed. At ICU admission, the area under the curve (AUC) was 0.766, with a sensitivity of 0.71 and specificity of 0.60; after extubation, the AUC was 0.841, with a sensitivity of 0.75 and specificity of 0.83. The models showed robust discrimination in both the development cohort and the validation cohort, and they were well calibrated and more accurate than reported risk models.ConclusionsICU patients who underwent noncardiac thoracic surgery were at high risk of developing PPCs. Prediction nomograms were constructed and they were more accurate than reported risk models, with excellent sensitivity and specificity. Moreover, these findings could help assess individual PPC risk and enhance postoperative management of patients.
| S-EPMC11360767 | biostudies-literature
Project description:BackgroundPostoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.MethodsA total of 905 patients who underwent thoracoscopy were randomly enrolled and divided into a training cohort and a validation cohort at 80%:20%. The training cohort was used to develop a nomogram model, and the validation cohort was used to validate the model. Univariate and multivariable logistic regression were applied to screen risk factors for PPCs, and the nomogram was incorporated in the training cohort. The discriminative ability and calibration of the nomogram for predicting PPCs were assessed using C-indices and calibration plots.ResultsAmong the patients, 207 (22.87%) presented PPCs, including 166 cases in the training cohort and 41 cases in the validation cohort. Using backward stepwise selection of clinically important variables with the Akaike information criterion (AIC) in the training cohort, the following seven variables were incorporated for predicting PPCs: American Society of Anesthesiologists (ASA) grade III/IV, operation time longer than 180 min, one-lung ventilation time longer than 60 min, and history of stroke, heart disease, chronic obstructive pulmonary disease (COPD) and smoking. With incorporation of these factors, the nomogram achieved good C-indices of 0.894 (95% confidence interval (CI) [0.866-0.921]) and 0.868 (95% CI [0.811-0.925]) in the training and validation cohorts, respectively, with well-fitted calibration curves.ConclusionThe nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.
| S-EPMC8572520 | biostudies-literature
Project description:BackgroundPostoperative pulmonary complications often lead to increased mortality and financial burden. Residual paralysis plays a critical role in postoperative pulmonary complications. This meta-analysis was performed to determine whether sugammadex overmatches neostigmine in reducing postoperative pulmonary complications.MethodsPubMed, Embase, Web of Science, Medline through Ovid, Cochrane Library, Wanfang, China National Knowledge Infrastructure, and Chinese BioMedical Literature Databases were searched from their inception to 24 June, 2021. Random effects models were used for all analyses. Cochrane risk of bias tool was used to assess the quality of RCTs, while Newcastle Ottawa Quality Assessment Scale was used to assess for the quality of cohort studies.ResultsSeventeen studies were included in the meta-analysis. Pooled data from cohort studies showed reversing neuromuscular blocking with sugammadex had less risk of compound postoperative pulmonary complications (relative risk [RR]: 0.73; 95% confidence interval [CI]: 0.60-0.89; P = 0.002; I2 = 81%), pneumonia (RR: 0.64; 95% CI: 0.48-0.86; I2 = 42%) and respiratory failure (RR: 0.48; 95% CI: 0.41-0.56; I2 = 0%). However, pooled data from RCTs did not show any difference between the two groups in pneumonia (RR: 0.58; 95% CI: 0.24-1.40; I2 = 0%) and no respiratory failure was reported in the included RCTs. The difference was not found between sugammadex and neostigmine about atelectasis in pooled data from either RCTs (RR: 0.85; 95% CI: 0.69-1.05; I2 = 0%) or cohort studies (RR: 1.01; 95% CI: 0.87-1.18; I2 = 0%).ConclusionThe evidence of superiority of sugammadex was limited by the confounding factors in cohort studies and small scale of RCTs. Whether sugammadex precedes neostigmine in preventing pulmonary complications after surgery is still unknown. Well-designed RCTs with large scale are needed.RegistrationPROSPERO ( https://www.crd.york.ac.uk/PROSPERO/ ); CRD 42020191575.
| S-EPMC10325761 | biostudies-literature
Project description:BackgroundPostoperative pulmonary complications (PPCs) are important contributors to mortality and morbidity after surgery. The available predicting models are useful in preoperative risk assessment, but there is a need for validated tools for the early postoperative period as well. Lung ultrasound is becoming popular in intensive and perioperative care and there is a growing interest to evaluate its role in the detection of postoperative pulmonary pathologies.ObjectivesWe aimed to identify characteristics with the potential of recognizing patients at risk by comparing the lung ultrasound scores (LUS) of patients with/without PPC in a 24-h postoperative timeframe.MethodsObservational study at a university clinic. We recruited ASA 2-3 patients undergoing elective major abdominal surgery under general anaesthesia. LUS was assessed preoperatively, and also 1 and 24 h after surgery. Baseline and operative characteristics were also collected. A one-week follow up identified PPC+ and PPC- patients. Significantly differing LUS values underwent ROC analysis. A multi-variate logistic regression analysis with forward stepwise model building was performed to find independent predictors of PPCs.ResultsOut of the 77 recruited patients, 67 were included in the study. We evaluated 18 patients in the PPC+ and 49 in the PPC- group. Mean ages were 68.4 ± 10.2 and 66.4 ± 9.6 years, respectively (p = 0.4829). Patients conforming to ASA 3 class were significantly more represented in the PPC+ group (66.7 and 26.5%; p = 0.0026). LUS at baseline and in the postoperative hour were similar in both populations. The median LUS at 0 h was 1.5 (IQR 1-2) and 1 (IQR 0-2; p = 0.4625) in the PPC+ and PPC- groups, respectively. In the first postoperative hour, both groups had a marked increase, resulting in scores of 6.5 (IQR 3-9) and 5 (IQR 3-7; p = 0.1925). However, in the 24th hour, median LUS were significantly higher in the PPC+ group (6; IQR 6-10 vs 3; IQR 2-4; p < 0.0001) and it was an independent risk factor (OR = 2.6448 CI95% 1.5555-4.4971; p = 0.0003). ROC analysis identified the optimal cut-off at 5 points with high sensitivity (0.9444) and good specificity (0.7755).ConclusionPostoperative LUS at 24 h can identify patients at risk of or in an early phase of PPCs.
| S-EPMC7807225 | biostudies-literature
Project description:AimPostoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.MethodsWe consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.ResultsThe derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655-0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883-0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632-0.633) and the AUROC was 0.889(95%CI, 0.888-0.889).ConclusionsThis study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.
| S-EPMC9395933 | biostudies-literature
Project description:BackgroundCompared with historic ventilation strategies, modern lung-protective ventilation includes lower tidal volumes (VT), lower driving pressures, and application of positive end-expiratory pressure (PEEP). The contributions of each component to an overall intraoperative protective ventilation strategy aimed at reducing postoperative pulmonary complications have neither been adequately resolved, nor comprehensively evaluated within an adult cardiac surgical population. The authors hypothesized that a bundled intraoperative protective ventilation strategy was independently associated with decreased odds of pulmonary complications after cardiac surgery.MethodsIn this observational cohort study, the authors reviewed nonemergent cardiac surgical procedures using cardiopulmonary bypass at a tertiary care academic medical center from 2006 to 2017. The authors tested associations between bundled or component intraoperative protective ventilation strategies (VT below 8 ml/kg ideal body weight, modified driving pressure [peak inspiratory pressure - PEEP] below 16 cm H2O, and PEEP greater than or equal to 5 cm H2O) and postoperative outcomes, adjusting for previously identified risk factors. The primary outcome was a composite pulmonary complication; secondary outcomes included individual pulmonary complications, postoperative mortality, as well as durations of mechanical ventilation, intensive care unit stay, and hospital stay.ResultsAmong 4,694 cases reviewed, 513 (10.9%) experienced pulmonary complications. After adjustment, an intraoperative lung-protective ventilation bundle was associated with decreased pulmonary complications (adjusted odds ratio, 0.56; 95% CI, 0.42-0.75). Via a sensitivity analysis, modified driving pressure below 16 cm H2O was independently associated with decreased pulmonary complications (adjusted odds ratio, 0.51; 95% CI, 0.39-0.66), but VT below 8 ml/kg and PEEP greater than or equal to 5 cm H2O were not.ConclusionsThe authors identified an intraoperative lung-protective ventilation bundle as independently associated with pulmonary complications after cardiac surgery. The findings offer insight into components of protective ventilation associated with adverse outcomes and may serve as targets for future prospective interventional studies investigating the impact of specific protective ventilation strategies on postoperative outcomes after cardiac surgery.
| S-EPMC6800803 | biostudies-literature
Project description:Pulmonary hypertension (PH) has diverse causes with heterogeneous physiology compelling distinct management. Differentiating patients with primarily elevated pulmonary vascular resistance (PVR) from those with PH predominantly because of elevated left-sided filling pressure is critical.We reviewed hemodynamics, echocardiography, and clinical data for 108 patients seen at a referral PH clinic with transthoracic echocardiogram and right heart catheterization within 1 year. We derived a simple echocardiographic prediction rule to allow hemodynamic differentiation of PH attributed to pulmonary vascular disease (PH(PVD), defined as pulmonary artery wedge pressure [PAWP]?15 mm Hg and PVR>3 WU). Age averaged 61.3±14.8 years, ?PAWP and PVR were 16.4±7.1 mm Hg and 6.3±4.0 WU, respectively, and 52 (48.1%) patients fulfilled PH(PVD) hemodynamic criteria. The derived prediction rule ranged from -2 to +2 with higher scores suggesting higher probability of PH(PVD): +1 point for left atrial anterior-posterior dimension <3.2 cm; +1 for presence of a mid systolic notch or acceleration time <80 ms; -1 for lateral mitral E:e'>10; -1 for left atrial anterior-posterior dimension >4.2 cm. PVR increased stepwise with score (for -2, 0, and +2, ?PVR were 2.5, 4.5, and 8.1 WU, respectively), whereas the inverse was true for pulmonary artery wedge pressure (corresponding ?PAWP were 21.5, 16.5, and 10.4 mm Hg). Among subjects with complete data, the score had an area under the curve (AUC) of 0.921 for PH(PVD). A score ?0 had 100% sensitivity and 69.3% positive predictive value for PH(PVD), with 62.3% specificity. No patients with a negative score had PH(PVD). Patients with a negative score and acceleration time >100 ms had normal PVR (?PVR=1.8 WU, range=0.7-3.2 WU).We present a simple echocardiographic prediction rule that accurately defines PH hemodynamics, facilitates improved screening and focused clinical investigation for PH diagnosis and management.
| S-EPMC3505751 | biostudies-literature
Project description:Multidrug-resistant tuberculosis (MDR-TB), resistance to at least isoniazid and rifampin, is a worldwide problem.To develop a clinical prediction rule to stratify risk for MDR-TB among patients with pulmonary tuberculosis.Derivation and internal validation of the rule among adult patients prospectively recruited from 37 health centers (Perú), either a) presenting with a positive acid-fast bacillus smear, or b) had failed therapy or had a relapse within the first 12 months.Among 964 patients, 82 had MDR-TB (prevalence, 8.5%). Variables included were MDR-TB contact within the family, previous tuberculosis, cavitary radiologic pattern, and abnormal lung exam. The area under the receiver-operating curve (AUROC) was 0.76. Selecting a cut-off score of one or greater resulted in a sensitivity of 72.6%, specificity of 62.8%, likelihood ratio (LR) positive of 1.95, and LR negative of 0.44. Similarly, selecting a cut-off score of two or greater resulted in a sensitivity of 60.8%, specificity of 87.5%, LR positive of 4.85, and LR negative of 0.45. Finally, selecting a cut-off score of three or greater resulted in a sensitivity of 45.1%, specificity of 95.3%, LR positive of 9.56, and LR negative of 0.58.A simple clinical prediction rule at presentation can stratify risk for MDR-TB. If further validated, the rule could be used for management decisions in resource-limited areas.
| S-EPMC2920322 | biostudies-other