Dataset Information

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery.

ABSTRACT:

Background

Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.

Methods

A 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.

Results

Among 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.

Conclusion

The nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.

SUBMITTER: Wang B

PROVIDER: S-EPMC8572520 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery.

Wang Bin B Chen Zhenxing Z Zhao Ru R Zhang Li L Zhang Ye Y

PeerJ 20211104

<h4>Background</h4>Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.<h4>Methods</h4>A 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 multivaria ...[more]

PMID: 34760381

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Project description:BackgroundPostoperative pulmonary complications (PPCs) significantly increase the morbidity and mortality in elderly patients with lung cancer. Considering the adverse effects of PPCs, we aimed to derive and validate a nomogram to predict pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer and to assist surgeons in optimizing patient-centered treatment plans.MethodsThe study enrolled 854 eligible elderly patients with lung cancer who underwent sub-lobectomy or lobectomy. A clinical prediction model for the probability of PPCs was developed using univariate and multivariate analyses. Furthermore, data from one center were used to derive the model, and data from another were used for external validation. The model's discriminatory capability, predictive accuracy, and clinical usefulness were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, respectively.ResultsAmong the eligible elderly patients with lung cancer, 214 (25.06%) developed pulmonary complications after video-assisted thoracoscopic surgery. Age, chronic obstructive pulmonary disease, surgical procedure, operative time, forced expiratory volume in one second, and the carbon monoxide diffusing capacity of the lung were independent predictors of PPCs and were included in the final model. The areas under the ROC curves (AUC) of the training and validation sets were 0.844 and 0.796, respectively. Ten-fold cross-validation was used to evaluate the generalizability of the predictive model, with an average AUC value of 0.839. The calibration curve showed good consistency between the observed and predicted probabilities. The proposed nomogram showed good net benefit with a relatively wide range of threshold probabilities.ConclusionA nomogram for elderly patients with lung cancer can be derived using preoperative and intraoperative variables. Our model can also be accessed using the online web server https://pulmonary-disease-predictor.shinyapps.io/dynnomapp/. Combining both may help surgeons as a clinically easy-to-use tool for minimizing the prevalence of pulmonary complications after lung resection in elderly patients.

Project description:BackgroundThe ARISCAT score, a prospectively developed generic classification for postoperative pulmonary complications (PPCs), has shown excellent predictive performance in general surgery. However, there is no reliable classification instrument for PPCs prediciton in thoracic surgery.ObjectiveThis study aimed to develop and validate a novel nomogram for estimating the risk of pulmonary complications in older patients (≥ 65 years) within 30 days after NCTS.MethodsA nomogram was developed using predefined candidate predictors of 30-day PPCs. It was fitted with least absolute shrinkage and selection operator and logistic regression methods. Internal validation was performed using a bootstrap-resampling approach, while external validation used an independent, temporally separated cohort. The model's performance was assessed based on its discriminative potential (area under the receiver operating characteristic curve [AUC]), predictive ability (calibration plots), and clinical utility (net benefit).ResultsIn the development (n = 1449) and validation (n = 449) cohorts, 34.9% and 31.4% of patients, respectively, developed pulmonary complications 30 days post-surgery. The final nomogram incorporated eight predictors (age, surgical approach, desaturation of < 92% for more than 2 min, duration of surgery, smoking status, FEV1/FVC%, respiratory infection in the last 30 days, and neoadjuvant chemotherapy). The nomogram showed excellent discrimination (AUC = 0.866, 95% confidence interval [CI], 0.846-0.885), calibration (Hosmer- Lemeshow test, P = 0.97) and overall performance (Brier score = 0.014) in the development cohort. Similar results were observed in the external validation cohort (AUC = 0.825, 95% CI, 0.786-0.864). A decision curve analysis indicated that the nomogram offers a positive net benefit compared with the ARISCAT and LAS VEGAS scores.ConclusionsThis novel nomogram can reliably identify older patients with a high risk for pulmonary complications within 30 days after NCTS.Trial registrationChiCTR2100051170.

Project description:BackgroundPostoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population.MethodsA total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA).ResultsA total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO2) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719-0.762), and it was 0.703 for the validation cohort (95% CI: 0.657-0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort.ConclusionThis study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions.

Project description:ObjectivesTo evaluate the occurrence, risk factors, and outcomes of post PCNL (Percutaneous Nephrolithotomy) Acute Kidney Injury (AKI), with a secondary goal of developing a nomogram for post-PCNL AKI prediction.MethodsA prospective observational study was conducted enrolling 333 patients who underwent PCNL between February 2022 and February 2023. Patient demographics, comorbidities, perioperative lab parameters, stone characteristics, intraoperative details, and postoperative AKI were assessed. Logistic regression analyses were employed to construct a nomogram for predicting post-PCNL AKI.Results40 patients (12.4%) experienced postoperative AKI, with recovery observed in all cases during the 3-month follow-up. Female gender (p = 0.002), hypertension(p = 0.022), higher serum uric acid levels(p = 0.003), staghorn calculi(p = 0.001), higher Hounsfield Units(p = 0.013), bilateral PCNL(p < 0.001), larger tract size(p = 0.017), longer operative time(p < 0.001), greater stone volume(p = 0.025), higher baseline serum creatinine levels(p < 0.001), higher postoperative total leukocyte count(p = 0.005), and postoperative fever(p < 0.001) were significantly associated with the AKI group. Regression analysis identified female gender (OR = 0.26, p = 0.035), higher serum uric acid levels(OR = 1.62, p = 0.013), bilateral PCNL(OR = 12.55, p < 0.001), longer operation time(OR = 1.02, p = 0.047), and larger stone volume(OR = 1.12, p = 0.015) as independent risk factors for postoperative AKI. The internally validated nomogram(n = 70) for predicting AKI demonstrated excellent diagnostic performance, with an area under the ROC curve of 0.984(95% CI, p < 0.001).ConclusionAKI occurs in approximately 12% of patients undergoing PCNL. We identified several significant predictors of post-PCNL AKI, including female gender, hypertension, hyperuricemia, higher Hounsfield units, larger stone volume, bilateral PCNL, larger access tract size, and longer operative time. Awareness of these factors is crucial for optimizing management and improving patient outcomes.

Project description:BackgroundVideo-assisted thoracoscopic surgery (VATS) often leads to a significant number of postoperative pulmonary complications (PPCs). The diaphragm is the primary muscle involved in respiration. Diaphragm ultrasound is increasingly used as a noninvasive and portable tool for evaluating diaphragmatic contractile function. The diaphragm excursion difference (DED) provides a valuable predictive measure for clinical outcomes. This prospective, observational study aimed to evaluate the predictive feasibility of DED in relation to PPCs in patients undergoing VATS.MethodsBetween March and August 2023, a total of 151 patients undergoing VATS were enrolled in the study. Each patient underwent diaphragm ultrasound examinations both before anesthesia and within one hour after extubation. During these examinations, diaphragm excursion (DE) was recorded during quiet breathing (QB) and deep breathing (DB). The DED and diaphragm excursion fraction (DEF) were calculated at these two time points. The primary outcome measured was the incidence of major pulmonary complications occurring within seven days after surgery, while secondary outcomes included the rate of diaphragm dysfunction, duration of hospitalization, oxygenation status, and pain scores on the first and second postoperative days.ResultsData from 151 patients were analyzed, revealing that 32 patients (21%) developed PPCs. Patients who developed PPCs exhibited a significantly lower postoperative diaphragm excursion during DB (2.27±0.59 vs. 3.31±0.99, P<0.001). They also showed a lower postoperative DED (0.94±0.44 vs. 1.94±0.91, P<0.001) and a higher DEF (0.59±0.13 vs. 0.44±0.12, P<0.001) compared to those without PPCs. In receiver operating characteristic curve analysis, the postoperative DED demonstrated a high sensitivity of 90.6% and a lower specificity of 64.7% for predicting PPCs, with an area under the curve (AUC) of 0.860 and a 95% confidence interval (CI) ranging from 0.788 to 0.926.ConclusionsAfter VATS, lower postoperative DED may serve as an additional marker. This can help predict the risk of pulmonary complications.

Project description:ImportanceThe Centers for Medicare and Medicaid Services have implemented penalties for hospitals with above-average readmission rates under the Hospital Readmissions Reductions Program. These changes will likely be extended to affect postoperative readmissions in the future.ObjectivesTo identify variables that place patients at risk for readmission, develop a predictive nomogram, and validate this nomogram.Design, setting, and participantsRetrospective review and prospective validation of a predictive nomogram. A predictive nomogram was developed with the linear predictor method using the American College of Surgeons National Surgical Quality Improvement Program database paired with institutional billing data for patients who underwent nonemergent inpatient general surgery procedures. The nomogram was developed from August 1, 2006, through December 31, 2011, in 2799 patients and prospectively validated from November 1, 2013, through December 19, 2013, in 255 patients at a single academic institution. Area under the curve and positive and negative predictive values were calculated.Main outcomes and measuresThe outcome of interest was readmission within 30 days of discharge following an index hospitalization for a surgical procedure.ResultsBleeding disorder (odds ratio, 2.549; 95% CI, 1.464-4.440), long operative time (odds ratio, 1.601; 95% CI, 1.186-2.160), in-hospital complications (odds ratio, 16.273; 95% CI, 12.028-22.016), dependent functional status, and the need for a higher level of care at discharge (odds ratio, 1.937; 95% CI, 1.176-3.190) were independently associated with readmission. The nomogram accurately predicted readmission (C statistic = 0.756) in a prospective evaluation. The negative predictive value was 97.9% in the prospective validation, while the positive predictive value was 11.1%.Conclusions and relevanceDevelopment of an online calculator using this predictive model will allow us to identify patients who are at high risk for readmission at the time of discharge. Patients with increased risk may benefit from more intensive postoperative follow-up in the outpatient setting.

Dataset Information

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery.

Background

Methods

Results

Conclusion

Publications

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery.

Similar Datasets

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