Readmission After Lobectomy for Lung Cancer: Not All Complications Contribute Equally

Modern fluid management in thoracic surgery

PURPOSE OF REVIEW

To provide an approach to perioperative fluid management for lung resection patients that incorporates the entire patient pathway in the context of international guidelines on enhanced recovery after surgery (ERAS).

RECENT FINDINGS

The concern with intraoperative fluid management is that giving too little or too much fluid is associated with worse outcomes after lung resection. However, it has not emerged as a key care element in thoracic ERAS programs probably due to the influence of other ERAS elements. Carbohydrate loading 2 h before surgery and the allowance of water until just prior to induction ensures the patient is both well hydrated and metabolically normal when they enter the operating room. Consequently, maintaining a euvolemic state during anesthesia can be achieved without goal-directed fluid therapy despite the recommendations of some guidelines. Intravenous fluids can be safely stopped in the immediate postoperative period.

SUMMARY

The goal of perioperative euvolemia can be achieved with the ongoing evolution and application of ERAS principles. A focus on the pre and postoperative phases of fluid management and a pragmatic approach to intraoperative fluid management negates the need for goal-directed fluid therapy in most cases.

Predictors of Readmission After Pulmonary Resection in Patients With Lung Cancer: A Systematic Review and Meta-analysis

Objective: Postoperative readmissions are considered an indicator of healthcare quality. The purpose of this study was to assess the factors associated with readmission following pulmonary resection for lung cancer. Methods: A comprehensive search was performed in PubMed, Web of science, the Cochrane Library, and databases of CNKI and Wanfang. We collected the factors associated with readmission following pulmonary resection from the included studies, and data analysis was conducted with STATA SE12.0 software. Results: A total of 11 studies (386 012 participants) were included. The meta-analysis results showed that age (standardized mean difference [SMD] = 0.093), male sex (odds ratio [OR] = 1.260), Charlson score (SMD = 1.408), forced expiratory volume in 1 second predicted (SMD = -0.203), congestive heart failure (OR = 1.708), peripheral vascular disease (OR = 1.436), and histology (OR = 0.804) were associated with readmission (P < .05), while hypertension was not. Patients with postoperative empyema, pneumonia, air leak, and arrhythmia (all P < .05) had higher odds of hospital readmission. Conclusion: The predictive factors for readmission can help in establishing individualized discharge and follow-up plans and programs for reducing hospital readmissions after pulmonary resection in patients with lung cancer.

Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

Background

Approximately 20% of patients with lung cancer would experience postoperative cardiopulmonary complications after anatomic lung resection. Current prediction models for postoperative complications were not suitable for Chinese patients. This study aimed to develop and validate novel prediction models based on machine learning algorithms in a Chinese population.

Methods

Patients with lung cancer receiving anatomic lung resection and no neoadjuvant therapies from September 1, 2018 to August 31, 2019 were enrolled. The dataset was split into two cohorts at a 7:3 ratio. The logistic regression, random forest, and extreme gradient boosting were applied to construct models in the derivation cohort with 5-fold cross validation. The validation cohort accessed the model performance. The area under the curves measured the model discrimination, while the Spiegelhalter z test evaluated the model calibration.

Results

A total of 1085 patients were included, and 760 were assigned to the derivation cohort. 8.4% and 8.0% of patients experienced postoperative cardiopulmonary complications in the two cohorts. All baseline characteristics were balanced. The values of the area under the curve were 0.728, 0.721, and 0.767 for the logistic, random forest and extreme gradient boosting models, respectively. No significant differences existed among them. They all showed good calibration (p > 0.05). The logistic model consisted of male, arrhythmia, cerebrovascular disease, the percentage of predicted postoperative forced expiratory volume in one second, and the ratio of forced expiratory volume in one second to forced vital capacity. The last two variables, the percentage of forced vital capacity and age ranked in the top five important variables for novel machine learning models. A nomogram was plotted for the logistic model.

Conclusion

Three models were developed and validated for predicting postoperative cardiopulmonary complications among Chinese patients with lung cancer. They all exerted good discrimination and calibration. The percentage of predicted postoperative forced expiratory volume in one second and the ratio of forced expiratory volume in one second to forced vital capacity might be the most important variables. Further validation in different scenarios is still warranted.

Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: A retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland

Unplanned readmissions shortly after discharge from hospital are common in chronic diseases. The risk of readmission has been shown to be related both to hospital care, e.g., medical complications, and to patients’ resources and abilities to manage the chronic disease at home and to make appropriate use of outpatient medical care. Despite a growing body of evidence on social determinants of health and health behaviour, little is known about the impact of social and contextual factors on readmission rates. The objective of this study was to analyse possible effects of educational, financial and social resources of patients with different chronic health conditions on unplanned 30 day-readmission risks. The study made use of nationwide inpatient hospital data that was linked with Swiss census data. The sample included n = 62,109 patients aged 25 and older, hospitalized between 2012 and 2016 for one of 12 selected chronic conditions. Multivariate logistic regressions analysis was performed. Our results point to a significant association between social factors and readmission rates for patients with chronic conditions. Patients with upper secondary education (OR = 1.26, 95% CI: 1.11, 1.44) and compulsory education (OR = 1.51, 95% CI: 1.31, 1.74) had higher readmission rates than those with tertiary education when taking into account demographic, social and health status factors. Having private or semi-private hospital insurance was associated with a lower risk for 30-day readmission compared to patients with mandatory insurance (OR = 0.81, 95% CI: 0.73, 0.90). We did not find a general effect of social resources, measured by living with others in a household, on readmission rates. The risk of readmission for patients with chronic conditions was also strongly predicted by type of chronic condition and by factors related to health status, such as previous hospitalizations before the index hospitalization (+77%), number of comorbidities (+15% higher probability per additional comorbidity) as well as particularly long hospitalizations (+64%). Stratified analysis by type of chronic condition revealed differential effects of social factors on readmissions risks. Compulsory education was most strongly associated with higher odds for readmission among patients with lung cancer (+142%), congestive heart failure (+63%) and back problems (+53%). We assume that low socioeconomic status among patients with chronic conditions increases the risk of unplanned 30-day readmission after hospitalisation due to factors related to their social situation (e.g., low health literacy, material deprivation, high social burden), which may negatively affect cooperation with care providers and adherence to recommended therapies as well as hamper active participation in the medical process and the development of a shared understanding of the disease and its cure. Higher levels of comorbidity in socially disadvantaged patients can also make appropriate self-management and use of outpatient care more difficult. Our findings suggest a need for increased preventive measures for disadvantaged populations groups to promote early detection of diseases and to remove financial or knowledge-based barriers to medical care. Socially disadvantaged patients should also be strengthened more in their individual and social resources for coping with illness.

Early and Late Readmission after Enhanced Recovery Thoracoscopic Lobectomy

OBJECTIVES

The purpose of this study was to describe the incidence and reasons for early (0-30 days) and late (31-90 days) readmission after enhanced recovery video-assisted thoracoscopic surgery lobectomy.

METHODS

We performed a retrospective analysis of prospectively collected consecutive VATS lobectomy data in an institutional database from January 2019 until December 2020. All reasons for readmission with complete follow-up were individually evaluated. Univariable and multivariable analyses were used to assess predictors.

RESULTS

In total 508 patients were included and median length of stay after surgery was 3 days. Early and late readmission were 77 (15%) and 54 (11%), respectively. Multiple readmissions during postoperative 0-90 days were 33 (7%). Pneumonia (19.8%) and pneumothorax (18.3%) were the dominant reasons for early readmission, and side effects to adjuvant chemotherapy (22.0%) for late readmission. In multivariable analyses, current smoking (P = 0.001), alcohol abuse (P = 0.024) and chronic obstructive pulmonary disease (P = 0.019) were predictors for early readmission, while (Clavien-Dindo I-II grade gastrointestinal complicationspredicted late readmission (P = 0.006) and multiple readmissions (P = 0.007). Early discharge (< 3 days) was not a predictor for readmission. Early readmission does not increase late readmission.

CONCLUSIONS

Early and late readmission are frequent despite of following enhanced recovery programs after video-assisted thoracoscopic lobectomy. Pulmonary complications and adjuvant chemotherapy are the most predominant reasons for early and late readmission.

[A Nomogram for Prediction of Complications Based on TM&M System of VATS Major Lung Surgery for Lung Cancer]

BACKGROUND

Postoperative complications are an important cause of death after lung resection. At present, the adoption of video assisted thoracoscopic surgery (VATS) for lung cancer in China is increasing every year, but the prediction model of postoperative complications of VATS for lung cancer is still lack of evidence based on large sample database. In this study, Thoracic Mortality and Morbidity (TM&M) classification system was used to comprehensively describe the postoperative complications of VATS major lung resection in our center, and the prediction model of complications was established and verified. The model can provide basis for the prevention and intervention of postoperative complications in such patients, and accelerate the recovery of patients.

METHODS

The clinical data of patients underwent VATS major lung resection in our center from January 2007 to December 2018 were collected retrospectively. Only patients with stage I-III lung cancer were included. The postoperative complications were registered strictly by TM&M classification system. The patients were divided into two groups according to the operation period: the early phase group (From 2007 to 2012) and the late phase group (From 2013 to 2018). The baseline data of the two groups were matched by propensity score matching. After matching, binary logistic regression analysis was used to establish the prediction model of complications, and bootstrap internal sampling was used for internal verification.

RESULTS

A total of 2,881 patients with lung cancer were included in the study, with an average age of (61.0±10.1) years, including 180 major complications (6.2%). Binary Logistic regression analysis of 1,268 matched patients showed: age (OR=1.04, 95%CI: 1.02-1.06, P<0.001), other period (OR=0.62, 95%CI: 0.49-0.79, P<0.001), pathological type (OR=1.73, 95%CI: 1.24-2.41, P=0.001), blood loss (OR=1.001, 95%CI: 1.000-1.003, P=0.03), dissected lymph nodes (OR=1.022, 95%CI: 1.00-1.04, P=0.005) were independent risk factors for postoperative complications. The ROC curve indicates that the model has good discrimination (C-index=0.699), and the C-index is 0.680 verified by bootstrap internal sampling for 1,000 times. The calibration curve shows a good calibration of the prediction model.

CONCLUSIONS

TM&M system can comprehensively and accurately report the postoperative complications of thoracoscopic lung cancer surgery. Age, operative period, pathological type, intraoperative bleeding and dissected lymph nodes were independent risk factors for postoperative complications of VATS major lung resection for lung cancer. The established complication prediction model has good discrimination and calibration.

Does Race or Ethnicity Impact Complications After Pulmonary Lobectomy for Patients With Lung Cancer?

BACKGROUND

Racial disparity in surgical access and postoperative outcomes after pulmonary lobectomy continues to be a concern and target for improvement; however, evidence of independent impact of race on complications is lacking. The objective of this study was to investigate the impact of race/ethnicity on surgical outcomes after lobectomy for lung cancer and estimate the distribution of racial/ethnic groups among expected resectable lung cancer cases using a large national database.

METHODS

Patients who underwent lobectomy for lung cancer between 2005 and 2016 were identified in the American College of Surgeon National Surgical Quality Improvement Program. Preoperative characteristics and postoperative outcomes were compared between race/ethnicity groups in all patients and in propensity-matched cohorts, controlling for pertinent risk factors. Distribution of each race/ethnicity in the database was calculated relative to estimated numbers of patients with resectable lung cancer in the United States.

RESULTS

A total of 10,202 patients (age 67.6 ± 9.7, 46.7% male, 86.4% white) underwent nonemergent lobectomy (46.8% thoracoscopic). Blacks had higher rates of baseline risk factors. In propensity score-matched cohorts of whites, blacks, and Hispanics/Asians (n = 498 each), postoperatively, blacks had higher rates of prolonged intubation and longer hospital stay while whites had a higher rate of pneumonia. Race was independently associated with these adverse outcomes on multivariate analysis. Proportion of blacks and Hispanics in the American College of Surgeon National Surgical Quality Improvement Program was lower than their respective proportion of resectable lung cancer in the United States.

CONCLUSIONS

In a large national-level surgical database, there was lower than expected representation of black and Hispanic patients. Black race was independently associated with extended length of stay and prolonged intubation, whereas white was independently associated with postoperative pneumonia.