Skip to main content
Download PDF
Download ePub

Factors associated with safe and successful postoperative day 1 discharge after lung operations: a systematic review and meta-analysis

Journal of Cardiothoracic Surgery volume 19, Article number: 91 (2024) Cite this article

Abstract

Background

A shorter length of stay (LOS) is associated with fewer hospital-acquired adverse conditions and decreased utilization of hospital resources. While modern perioperative care protocols have enabled some ambitious surgical teams to achieve discharge as early as within postoperative day 1 (POD1), most other teams remain cautious about such an approach due to the perceived risk of missing postoperative complications and increased readmission rates. We aimed to identify factors that would help guide surgical teams aiming for safe and successful POD1 discharge after lung resection.

Methods

We searched the PubMed, Embase, Scopus, Web of Science and CENTRAL databases for articles comparing perioperative characteristics in patients discharged within POD1 (DWPOD1) and after POD1 (DAPOD1) following lung resection. Meta-analysis was performed using a random-effects model.

Results

We included eight retrospective cohort studies with a total of 216,887 patients, of which 22,250 (10.3%) patients were DWPOD1. Our meta-analysis showed that younger patients, those without cardiovascular and respiratory comorbidities, and those with better preoperative pulmonary function are more likely to qualify for DWPOD1. Certain operative factors, such as a minimally invasive approach, shorter operations, and sublobar resections, also favor DWPOD1. DWPOD1 appears to be safe, with comparable 30-day mortality and readmission rates, and significantly less postoperative morbidity than DAPOD1.

Conclusions

In select patients with a favorable preoperative profile, DWPOD1 after lung resection can be achieved successfully and without increased risk of adverse outcomes such as postoperative morbidity, mortality, or readmissions.

Peer Review reports

Introduction

Approximately 120,000 lung resections for lung cancer are performed annually in the United States [1]. Minimally invasive techniques such as video-assisted (VATS) and robotic-assisted (RATS) thoracoscopic surgery are increasingly becoming the standard of care, as they are associated with reduced complications, postoperative pain, and length of hospital stay (LOS) compared to open thoracotomy [2,3,4,5].

The average LOS after lung resection ranges from 4–5 days in the US. This is an improvement from the 7-day average LOS a decade ago, when open thoracotomy was more common [6]. A shorter LOS is associated with fewer hospital-acquired infections and decreased utilization of hospital resources [6], with approximately $1500 being saved per day reduction in LOS [7, 8]. To achieve earlier hospital discharges with optimal postoperative outcomes, healthcare systems in the US have developed various fast-track perioperative care protocols such as Enhanced Recovery after Surgery (ERAS®) [9, 10]. In thoracic surgery, these enhanced recovery protocols include recommendations for early removal of chest tubes, appropriate lung physiotherapy, and pain management to help effectuate earlier discharge [9, 11,12,13].

Measures such as these have enabled thoracic surgery teams in several institutions to achieve discharge as early as within the first postoperative day (POD1) without increasing readmissions [14,15,16,17]. However, most other teams remain cautious about such an approach due to the perceived risk of missing postoperative complications and increased readmission rates [16, 18]. To aim for successful and safe POD1 discharge in one’s practice, surgical teams must be able to recognize what patient characteristics may allow for a POD1 discharge. This study aims to identify factors that would help guide surgical teams aiming for successful POD1 discharge after lung resection.

Methods

This meta-analysis adheres to the guidelines in the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [19]. The completed PRISMA Checklist (2020) is available in Additional file 1: Section 1. An institutional review board approval was not required for this study as the data used is publicly available. This study was prospectively registered with PROSPERO (CRD42023406389).

Search strategy

A comprehensive search of electronic databases including PubMed, Scopus, Embase, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL), was conducted from inception to December 2022. Studies were also identified by manual searching and snowballing by reviewing bibliographies of relevant articles. Conference proceedings of published abstracts were also examined to identify grey literature. No restriction on time, language, study design, or sample size was placed. A detailed search strategy is mentioned in Additional file 1: Section 2.

Study selection

Articles were shortlisted based on the following inclusion criteria:

  1. (1)

    Observational or interventional studies reporting original data.

  2. (2)

    Studies comparing data based on the following PICO (participants, intervention, comparator, outcome) parameters:

    • P: Adult patients who underwent any type of lung resection (including anatomic and non-anatomic resections)

    • I: Discharges within POD1 (DWPOD1)

    • C: Discharges after POD1 (DAPOD1)

    • O: Preoperative patient characteristics, intraoperative variables, or postoperative outcomes.

The exclusion criteria are as follows:

  1. (1)

    Studies including patients undergoing lung transplant.

  2. (2)

    Articles that failed to stratify outcomes based on groups of interest (DWPOD1 and DAPOD1).

  3. (3)

    Articles with no extractable or analyzable data based on POD1 discharge.

  4. (4)

    Articles published prior January 2000.

Screening process and data extraction

All articles retrieved from the systematic search were exported to Endnote Reference Manager (Version X4; Clarivate Analytics, Philadelphia, Pennsylvania, USA) where duplicates were identified and removed. Two authors (HR and AIA) independently reviewed the articles initially on the bases of the title and abstract. Eventually, the full text of the shortlisted articles was read to ensure inclusion based on the eligibility criteria. A third author (ASF or RSM) was consulted in case of a discrepancy. General article data and perioperative variables were extracted from all short-listed articles by two independent reviewers (HR and AIA), with any conflict resolved by consensus with a third reviewer (ASF or RSM). Anatomic lung resection (AR) was defined by studies to include both lobectomies and segmentectomies.

In case of missing data in the article main text or Additional file 1, corresponding authors were contacted to retrieve the additional data for analysis. We also approximated means and standard deviations from medians and interquartile ranges where necessary, based on methodologies outlined by Wan et al. [20]. The continuous data that was approximated using these methods is shown in Additional file 1: Section 3.

Although two of the included studies, Drawbert et al. [14] and Mahenthiran et al. [21] performed propensity-score matched (PSM) analyses for postoperative mortality and readmission, only Drawbert et al. reported these data in a form suitable for meta-analysis. As such, no PSM data from Mahenthiran et al. could be incorporated into the meta-analysis. Moreover, for postoperative mortality and readmission, PSM data for Drawbert et al. was used instead of data from the unmatched cohort.

Two of the included studies, Mahenthiran et al. [21] and Patel et al. [18], had an overlapping patient cohort as they both used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) Database. For comparisons where both studies reported meta-analyzable data, we used the data from Patel et al. [18] because it included data from 2011 to 2019 (while Mahenthiran et al. only included data from 2011 to 2018).

As Towe et al. [22] stratified outcomes based on wedge resection (WR) and AR, data was extracted for the patients who underwent WR as only the WR group reported outcomes for patients DWPOD1. The remaining studies were comprised wholly of patient cohorts who underwent AR.

Quality assessment and certainty of evidence assessment

Two authors (HR and AIA) independently assessed the risk of bias in all included articles using the risk of bias in non-randomized studies-of interventions (ROBINS-I) tool for observational studies [23]. The certainty of evidence for each outcome was independently determined using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) approach [24] by two reviewers (HR and AIA) via the GRADE Pro Software (McMaster University and Evidence Prime Inc, Ontario, Canada). In case of any disagreements, a third reviewer was consulted (ASF). Publication bias was not included for any outcome given that the Cochrane Handbook advises not to generate funnel plots for outcomes with less than 10 studies [25], and our review quantitatively pooled 8 studies.

Statistical analysis

All statistical analysis was conducted using RevMan (version 5.4; Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2022). Pooled results were represented as risk ratios (RRs) with 95% confidence intervals (CIs) for dichotomous outcome variables, and as odds ratios (ORs) with 95% CI for all other dichotomous variables. Continuous outcomes were presented as mean differences (MDs) with 95% CI. We used the Mantel–Haenszel random effects model to report the pooled RRs/ORs and the inverse variance random effects model to report the pooled MDs. Random effects meta-analysis models were also employed to offset the impact of variable sample sizes across the studies. This is because, given the shared between-study variance used in random-effects models, they lead to a more balanced distribution of weights despite differences in sample size [26]. It is also worth noting that while associations between DWPOD1 and continuous variables were statistically tested, the overall size of the MD may not necessarily be meaningful due to many continuous data being approximated from non-parametric measures. Forest plots were generated for all outcomes with greater than or equal to 3 studies. A p < 0.05 was considered statistically significant in all cases.

Heterogeneity due to between-study differences was assessed using Tau2, which quantifies between-study variability in effect sizes, and Higgins I2 statistics, which quantifies the proportion of variability in effect sizes that is attributable to heterogeneity (I2 = 0 was considered negligible, 1–50% was considered minimal, 50–75% moderate, and > 75% substantial). Additionally, sensitivity analyses were performed whereby the meta-analysis was re-conducted for each outcome by removing each study individually and evaluating its effect on the significance of the pooled result.

Results

Study characteristics

We included eight studies [14,15,16, 18, 21, 22, 27, 28], all retrospective cohort studies, with a total of 216,887 patients (Fig. 1). Amongst these, 22,250 (10.3%) patients were discharged within POD1 (DWPOD1). All studies were conducted in the US and published in or after 2018. Six out of eight studies analyzed data from an existing multicentric database [14, 16, 18, 21, 27, 28], while the remaining two [15, 22] used institutional data. Two of the included studies performed propensity-score-matched analyses [14, 18]. All of the patients in Drawbert et al. [14], and the majority of patients in Greer et al. [27] (DWPOD1: 62.8%; DAPOD1: 64.7%) had Stage 1 malignancies, while the remaining studies did not provide information on TNM stage. Table 1 outlines relevant study characteristics of the articles included.

Fig. 1
figure 1

PRISMA Flowchart

Full size image
Table 1 Characteristics of included articles
Full size table

Demographics

All eight studies [14,15,16, 18, 21, 22, 27, 28] reported patient age and sex, while five studies [14, 18, 21, 22, 27] reported race. Meta-analysis revealed that younger age was associated with DWPOD1 (MD − 1.65 [95% CI − 2.56, − 0.75]; p < 0.001), while no significant associations were observed for sex and race (Additional file 1: Figs. 4.1–4.3).

Primary diagnosis, comorbid conditions and functional status

A total of five studies [15, 21, 22, 27, 28] reported data separately for malignant and benign lung pathology. A diagnosis of lung cancer was associated with a significantly lower likelihood of DWPOD1 (OR 0.68 [0.56, 0.82]; p < 0.001). On analysis of comorbidities, a history of hypertension (HTN) (OR 0.82 [0.73, 0.93]; p = 0.001), smoking (OR 0.68 [0.61, 0.76]; p < 0.001), congestive heart failure (CHF) (OR 0.74 [0.67, 0.81]; p < 0.001), chronic obstructive pulmonary disease (COPD) (OR 0.70 [0.58, 0.84]; p < 0.001), coronary artery disease (CAD) (OR 0.72 [0.69, 0.76]; p < 0.001), or peripheral vascular disease (PVD) (OR 0.72 [0.56, 0.91]; p = 0.007) was associated with significantly lower DWPOD1 rates. While none of the studies reported multimorbidity (i.e., the presence of multiple comorbidities within the same patient), Drawbert et al. [14] (Charlson/Deyo Comorbidity Score) and Tran et al. [28] (Elixhauser Comorbidity Index) reported composite comorbidity scores. While these were non-meta-analyzable, both studies showed that DAPOD1 was associated with greater composite comorbidity scores at baseline. A higher BMI was also significantly associated with a greater likelihood of DWPOD1 (MD 0.44 [0.12, 0.75]; p = 0.007). With regards to pulmonary function, higher preoperative percentage of predicted forced expiratory volume in the first second (FEV1) (MD 4.72 [1.58, 7.85]; p = 0.002) and diffusing capacity of the lungs for carbon monoxide (DLCO) (MD 4.91 [1.73, 8.09]; p = 0.002) were associated with a higher likelihood of DWPOD1. ASA (American Society of Anesthesiology) Physical Status Classification, diabetes mellitus, and preoperative steroid medications were not associated with DWPOD1. These results are depicted in Figs. 2, 3, and Additional file 1: Figs. 4.4–4.10.

Fig. 2
figure 2

Meta-Analyses of Cardiovascular Comorbidities. M–H, Mantel–Haenszel; CI, confidence interval; df, degrees of freedom; P, probability value

Full size image
Fig. 3
figure 3

Meta-Analyses of Respiratory Comorbidities. M–H, Mantel–Haenszel; SD, standard deviation; IV, inverse variance; CI, confidence interval; df, degrees of freedom; P, probability value

Full size image

Operative characteristics

Location of the mass was not significantly associated with POD1 discharge. However, lobar resections were significantly associated with a lower likelihood of DWPOD1, compared to sub-lobar resections such as segmentectomies (OR 0.35 [0.24, 0.51]; p < 0.001). A minimally invasive approach was associated with significantly greater rates of DWPOD1, compared to open thoracotomy (OR 6.17 [1.91, 19.93]; p < 0.001). Shorter operations were also associated with a greater likelihood of DWPOD1 (MD − 28.08 [− 41.65, − 14.51]; p < 0.001). These results are depicted in Fig. 4.

Fig. 4
figure 4

Meta-Analyses of Operative Characteristics. M–H, Mantel–Haenszel; SD, standard deviation; IV, inverse variance; CI, confidence interval; df, degrees of freedom; P, probability value

Full size image

Postoperative characteristics

Chest tube management strategies were described in three articles [15, 22, 27], none of which considered chest tube removal to be an absolute criterion for hospital discharge. These have been summarized in Additional file 1: Table 5.1. Patients in the DWPOD1 were less likely to be discharged with a chest tube in place (OR 0.38 [0.15, 0.91]) and develop air leaks that persisted > 5 days (OR 0.19 [0.08–0.046]). There were no significant differences in 30-day mortality (RR: 1.01 [0.50, 2.05]) or 30-day readmission (RR 0.84 [0.62, 1.14]) between the DWPOD1 and DAPOD1 groups, although DWPOD1 patients were less likely to experience major postoperative morbidity (RR 0.31 [0.24, 0.41]; p < 0.001). However, sensitivity analysis revealed that on removal of Drawbert et al. [14] (which was the only study to report a significantly higher rate of 30-day mortality and 30-day readmission in the DWPOD1 group and also the only study with PSM data for these outcomes) DWPOD1 patients were less likely to experience mortality (RR: 0.70 [0.51, 0.96]) or readmission (RR: 0.75 [0.62, 0.89]). These results are depicted in Figs. 5 and Additional file 1: Figs. 4.11–4.12. When replacing the PSM data from Drawbert et al. with the data from the unmatched cohort, the pooled meta-analyzed result is insignificant for both outcomes (mortality: 1.04 [0.44, 2.42]; and readmission: 0.83 [0.59, 1.17]). A summary of all meta-analyses is shown in Table 2.

Fig. 5
figure 5

Meta-Analyses of Postoperative Characteristics. M–H, Mantel–Haenszel; SD, standard deviation; IV, inverse variance; CI, confidence interval; df, degrees of freedom; P, probability value

Full size image
Table 2 Factors associated with DWPOD1 compared to DAPOD1 patients
Full size table

Heterogeneity

Substantial effect size variation attributable to inter-study heterogeneity was observed for the meta-analysis of age, HTN, COPD, FEV1, DLCO, type of resection, duration of operation, operative approach, and major postoperative morbidity. Moderate effect size variation attributable to inter-study heterogeneity was observed for the meta-analysis of nature of pathology and PVD. Minimal or low effect size variation attributable to inter-study heterogeneity was observed for the meta-analysis of CHD, CAD, BMI, and smoking history (Additional file 1: Section 6).

Additional sensitivity analyses

The relatively larger sample size of Tran et al. [28] accounted for its larger weight in the outcomes where it was pooled. As such, a sensitivity analysis was conducted to investigate its impact on these outcomes and to reduce bias. There was no change in the significance of the meta-analyzed result upon removal of Tran et al. [28] for any of the variables barring nature of the lung pathology (malignant vs. benign), where removing the study resulted in a non-significant result (OR 0.78 [0.51, 1.21]).

In addition, given that Towe et al. [22] was the only study which had data from patients who underwent WR, a sensitivity analysis was conducted to determine whether similar results would be obtained when pooling studies with purely AR patients. There was no change in the significance of the pooled result upon removal of Towe et al. in any of the variables except for race where removing the study resulted in a significant result (OR 0.92 [0.85, 1.00]; p = 0.04).

Additional details regarding these sensitivity analyses are present in Additional file 1: Section 7.

Risk of bias assessment (ROBINS-I)

5 [14,15,16, 21, 28] out of the 8 included studies were deemed to have an overall low risk of bias as per the ROBINS-I tool, with the remaining 3 [18, 22, 27] having a moderate risk of bias. The domain-wise results of the quality assessment are present in the Additional file 1: Section 8.

Certainty of evidence

The overall certainty of evidence was low to moderate. Of the 25 meta-analyzed comparisons, 8 were deemed to have a high certainty of evidence, 9 were deemed to have a moderate certainty of evidence, 6 were deemed to have a low certainty of evidence, and 2 were deemed to have a very low quality of evidence. The domain-wise GRADE evidence profile is available in Additional file 1: Section 9.

Discussion

In this meta-analysis, we aimed to identify factors associated with successful DWPOD1 after lung resection, both anatomic and non-anatomic, and evaluate outcomes after DWPOD1. Our results show that younger patients, those without cardiovascular and respiratory comorbids (HTN, CHF, PVD, CAD, COPD, smoking history), and better preoperative pulmonary function (FEV1 and DLCO) are more likely to qualify for DWPOD1. Interestingly, a higher BMI was found to favor DWPOD1. Certain operative factors, such as a minimally invasive approach, shorter operations, and sublobar resections, also favor DWPOD1. Lastly, DWPOD1 appears to be safe when implemented in a favorable patient cohort, with comparable 30-day mortality and readmission rates, and significantly less postoperative morbidity.

The patient characteristics that favor DWPOD1 likely do so by streamlining postoperative recovery after lung resection. Younger patients have better post-anesthesia cognitive recovery and are less frail, allowing for earlier and safer postoperative mobilization [29, 30]. Earlier ambulation facilitates DWPOD1 by improving pain and cardiorespiratory function. Moreover, older patients undergoing lung operations may also be more likely to require discharge to a non-home care or rehabilitation institution [31], the logistics of which may lead to a longer hospital LOS. Interestingly, higher BMI may favor quicker postoperative recovery—the so called “obesity paradox”—though mechanisms underlying this association remain unclear and require further exploration [32]. Our results provide thoracic surgery teams with a yardstick to assess overall suitability for deliberately expedited postoperative care where the team can be confident about a safe and successful DWPOD1. More research is needed to develop appropriate risk stratification systems that consider these key patient characteristics and allow surgeons to determine suitability for DWPOD1 in an objective and standardized manner. A preliminary attempt in this regard has been presented by Towe et al. [33], with their risk score being based on a multivariable regression model with constituent variables like those identified as significant in our study (patient age, BMI, CAD, COPD, operative approach, and duration of operation). Our results may help guide future iterations of such risk calculators.

Perhaps most importantly in the context of lung resection, preoperative pulmonary health and function appeared to be a strong predictor of successful DWPOD1. Lung resections may be associated with significant reductions in pulmonary function (on average about 22% reduction after lobectomy) [34]. It is thus no surprise that evidence-based clinical practice guidelines (EBCPGs) by the ERAS ® Society and the European Society of Thoracic Surgeons (ESTS) place heavy emphasis on maximizing pulmonary function [35]. A proactive approach to pulmonary prehabilitation, particularly in patients with decreased lung function, can help reduce hospital LOS and should potentially be considered as a routine component of care in ambitious surgical teams aiming for DWPOD1 [35].

In addition, chest tube management is a key component of an accelerated discharge program. None of the three studies describing chest tube removal strategies [15, 22, 27] considered tube removal or absence of an air leak to be absolute criteria for hospital discharge. While we concur with this general philosophy, there are other factors that must also be considered. The extent of resection and surgical approach may be important determinants of successful postoperative outpatient chest tube management. One Chinese study of 95 patients from 2020 suggested that outpatient chest tube management can be successfully achieved in select patients who undergo minimally invasive segmentectomies [36]. However, another study of 253 patients from 2022 suggested that discharge with a chest tube, irrespective of resection type, surgical approach, and functional status, may be associated with serious adverse outcomes including need for reoperation and empyema [37]. Moreover, patients and families must be provided with in-depth education regarding chest tube care at home, potential complications, and must be provided with an efficient and reliable means to contact the thoracic surgery team in case of concerns. In practices where the first routine outpatient follow-up after discharge occurs after approximately 5 days postoperatively, it may, therefore, be more prudent to wait an additional day (till POD2) for inpatient chest tube removal in a patient with a minor air leak/high chest tube output. The alternative is sending the patient with an indwelling tube and either scheduling an additional early visit for tube removal or waiting until the POD5 follow-up visit to do so—both cases pose an additional inconvenience to the patient.

We believe that DWPOD1 can be considered safe in patients with favorable preoperative baseline characteristics. There were no significant differences in 30-day mortality or 30-day readmission between the DWPOD1 and DAPOD1 groups, and DWPOD1 patients were less likely to experience major postoperative morbidity. Drawbert et al. [14] was the only study to report significantly worse postoperative outcomes amongst the DWPOD1 group, with this association seen only in low- and medium-volume centers. Interestingly, exclusion of Drawbert et al. from the meta-analysis resulted in DWPOD1 having significantly lower rates of 30-day mortality and 30-day readmission.

Lastly, we also believe that the decision to discharge a patient early should consider each individual patient’s personal circumstances. Elderly patients may have inadequate support structures at home and thus require skilled aid to assist them during the early postoperative period or may be discharged to a non-home facility. Both of these scenarios may be associated with additional costs, and thus the cost savings from hospital LOS reduction must be weighed against potential resultant out-of-hospital costs.

This study is not without its limitations. The meta-analysis for most variables demonstrated moderate-to-substantial heterogeneity, with this likely being a function of differences in patient case-mix and differences in data sources. Additionally, as several of the datasets used by the included articles were national datasets, there is the possibility of overlapping patient data between studies. The use of large databases also meant that the data in these articles were retrospective and non-granular. Only one study presented data for patients undergoing WR, precluding any pooled subgroup analysis exploring factors associated with DWPOD1 in a WR cohort exclusively. In addition, several key variables were not reported by the studies which is a known shortcoming of retrospective database studies [38]. These include patient frailty, diagnosis-related data (stage of malignancy), data on important postoperative in-patient milestones after lung operations (intravenous fluid administration, use of opiate analgesia and postoperative nausea and vomiting therapy, feeding, mobilization, and timing of chest drain and catheter removal) [39]. In addition, the type of thoracotomy (postero-lateral i.e., conventional or muscle-sparing) was not specified in most studies. Moreover, as DWPOD1 currently seems to be a routine practice only amongst select surgical teams across the US, it is also worthwhile to explore surgeon-level practices, workflow protocols, and staffing models that enable expedited discharges. The impact of hospital-level factors, such as hospital operative volume, should also be evaluated, as there is evidence of a volume-outcome relationship for lung operations [40, 41].

Conclusion

Factors promoting DWPOD1 after lung resection include favorable preoperative patient characteristics, notably cardiovascular and pulmonary healthy function, and operative factors, such as a minimally invasive approach and shorter operations. In select patients, DWPOD1 can be achieved safely and successfully, without increased risk of postoperative morbidity, mortality, or readmissions. However, there is no single element that can predict whether patients are suitable for DWPOD1. The complex interplay of several preoperative and intraoperative factors necessitates the development of appropriate risk calculators that considers key patient characteristics to determine suitability for DWPOD1. Future research must investigate additional key variables in the continuum of perioperative care, including postoperative in-patient milestones, surgeon factors, and workflow protocols.

Availability of data and materials

The data that support the findings of this study were sourced directly from the published studies included in this systematic review and meta-analysis.

Abbreviations

VATS:

Video-assisted thoracoscopic surgery

RATS:

Robotic-assisted thoracoscopic surgery

LOS:

Length of hospital stay

ERAS:

Enhanced recovery after surgery

POD1:

First postoperative day

PRISMA:

Preferred reporting items for systematic reviews and meta-analysis

CENTRAL:

Cochrane central register of controlled trials

DWPOD1:

Discharges within POD1

DAPOD1:

Discharges after POD1

ACS-NSQIP:

American college of surgeons national surgical quality improvement program

ROBINS-I:

Risk of bias in non-randomized studies-of interventions

GRADE:

Grading of recommendations, assessment, development, and evaluations

PSM:

Propensity-score-matched

RR:

Risk ratio

OR:

Odds ratio

MD:

Mean difference

CI:

Confidence interval

HTN:

Hypertension

CHF:

Congestive heart failure

COPD:

Chronic obstructive pulmonary disease

CAD:

Coronary artery disease

PVD:

Peripheral vascular disease

FEV1 :

Forced expiratory volume in the first second

DLCO:

Diffusing capacity of the lungs for carbon monoxide

ASA:

American society of anesthesiologists

EBCPGs:

Evidence-based clinical practice guidelines

ESTS:

European society of thoracic surgeons

TNM:

Tumor extent, nodal spread, presence of metastasis

References

  1. Cronin KA, Lake AJ, Scott S, Sherman RL, Noone AM, Howlader N, et al. Annual Report to the nation on the status of cancer, part I: national cancer statistics. Cancer. 2018;124(13):2785–800.

    Article  PubMed  Google Scholar 

  2. Ghaly G, Kamel M, Nasar A, Paul S, Lee PC, Port JL, et al. Video-assisted thoracoscopic surgery is a safe and effective alternative to thoracotomy for anatomical segmentectomy in patients with clinical stage I non-small cell lung Cancer. Annals Thorac Surg. 2016;101(2):465–72 (discussion 72).

    Article  Google Scholar 

  3. Tsubokawa N, Harada H, Takenaka C, Misumi K, Yamashita Y. Comparison of postoperative pain after different thoracic surgery approaches as measured by electrical stimulation. Thorac Cardiovasc Surg. 2015;63(6):519–25.

    Article  PubMed  Google Scholar 

  4. Klapper J, D’Amico TA. VATS versus open surgery for lung cancer resection: moving toward a minimally invasive approach. J Natl Compr Canc Netw. 2015;13(2):162–4.

    Article  PubMed  Google Scholar 

  5. Liu S, Li S, Tang Y, Chen R, Qiao G. Minimally invasive surgery vs. open thoracotomy for non-small-cell lung cancer with N2 disease: a systematic review and meta-analysis. Front Med. 2023;10:1152421.

    Article  Google Scholar 

  6. Giambrone GP, Smith MC, Wu X, Gaber-Baylis LK, Bhat AU, Zabih R, et al. Variability in length of stay after uncomplicated pulmonary lobectomy: is length of stay a quality metric or a patient metric?†. Eur J Cardiothorac Surg Off J Eur Assoc Cardiothorac Surg. 2016;49(4):e65-71.

    Article  Google Scholar 

  7. Fuller C. Reduction of intraoperative air leaks with Progel in pulmonary resection: a comprehensive review. J Cardiothorac Surg. 2013;8(1):90.

    Article  PubMed  PubMed Central  Google Scholar 

  8. United States Bureau of Labor Statistics. CPI Inflation Calculator. Available from: https://www.bls.gov/data/inflation_calculator.htm.

  9. Semenkovich TR, Hudson JL, Subramanian M, Kozower BD, editors. Enhanced recovery after surgery (ERAS) in thoracic surgery. Seminars in thoracic and cardiovascular surgery; 2018 Autumn;30(3):342–349. https://doi.org/10.1053/j.semtcvs.2018.06.001.

  10. Schmid S, Kaafarani M, Baldini G, Amir A, Costescu F, Shafiepour D, et al. Implication of a novel postoperative recovery protocol to increase day 1 discharge rate after anatomic lung resection. J Thorac Dis. 2021;13(11):6399–408.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fiore JF Jr, Bejjani J, Conrad K, Niculiseanu P, Landry T, Lee L, et al. Systematic review of the influence of enhanced recovery pathways in elective lung resection. J Thorac Cardiovasc Surg. 2016;151(3):708–15.

    Article  PubMed  Google Scholar 

  12. Kehlet H, Wilmore DW. Evidence-based surgical care and the evolution of fast-track surgery. Annals Surg. 2008;248(2):189–98.

    Article  Google Scholar 

  13. Madani A, Fiore JF Jr, Wang Y, Bejjani J, Sivakumaran L, Mata J, et al. An enhanced recovery pathway reduces duration of stay and complications after open pulmonary lobectomy. Surgery. 2015;158(4):899–910.

    Article  PubMed  Google Scholar 

  14. Drawbert HE, Hey MT, Tarrazzi F, Block M, Razi SS. Early discharge on postoperative day 1 following lobectomy for stage I non-small-cell lung cancer is safe in high-volume surgical centres: a national cancer database analysis. European J Cardiothorac Surg Off J European Assoc Cardiothorac Surg. 2022;61(5):1022–9.

    Article  Google Scholar 

  15. Geraci TC, Chang SH, Chen S, Ferrari-Light D, Cerfolio RJ. Discharging patients by postoperative day one after robotic anatomic pulmonary resection. Ann Thorac Surg. 2022;114(1):234–40.

    Article  PubMed  Google Scholar 

  16. Linden PA, Perry Y, Worrell S, Wallace A, Argote-Greene L, Ho VP, et al. Postoperative day 1 discharge after anatomic lung resection: a society of thoracic surgeons database analysis. J Thorac Cardiovasc Surg. 2020;159(2):667-78.e2.

    Article  CAS  PubMed  Google Scholar 

  17. Rosen JE, Salazar MC, Dharmarajan K, Kim AW, Detterbeck FC, Boffa DJ. Length of stay from the hospital perspective: practice of early discharge is not associated with increased readmission risk after lung cancer surgery. Ann Surg. 2017;266(2):383–8.

    Article  PubMed  Google Scholar 

  18. Patel DC, Leipzig M, Jeffrey Yang CF, Wang Y, Shrager JB, Backhus LM, et al. Early discharge after lobectomy for lung cancer does not equate to early readmission. Ann Thorac Surg. 2022;113(5):1634–40.

    Article  PubMed  Google Scholar 

  19. Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA group* tJAoim. Preferr Report Items Syst Rev Meta-analyses PRISMA State. 2009;151(4):264–9.

    Google Scholar 

  20. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14:1–13.

    Article  Google Scholar 

  21. Mahenthiran AK, Brajcich BC, Kim S, Bharat A, Odell DD. National assessment of early discharge after video-assisted thoracoscopic surgery for lung resection. J Surg Res. 2022;276:242–50.

    Article  PubMed  Google Scholar 

  22. Towe CW, Khil A, Ho VP, Perry Y, Argote-Greene L, Wu KM, et al. Early discharge after lung resection is safe: 10-year experience. J Thorac Dis. 2018;10(10):5870–8.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355: i4919.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Chinese J Evidence-Based Med. 2008;336(7650):924–6.

    Google Scholar 

  25. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions. Wiley; 2019. https://training.cochrane.org/handbook.

  26. Fixed- KS, Models R-E. Fixed- and random-effects models. Methods Mol Biol. 2022;2345:41–65. https://doi.org/10.1007/978-1-0716-1566-9_3.

    Article  CAS  Google Scholar 

  27. Greer S, Miller AD, Smith JS, Holcombe JM, Headrick JR Jr. Safety of next day discharge after lobectomy: have we broken the speed limit? Ann Thorac Surg. 2018;106(4):998–1001.

    Article  PubMed  Google Scholar 

  28. Tran Z, Chervu N, Williamson C, Verma A, Hadaya J, Gandjian M, et al. The impact of expedited discharge on 30-day readmission after lung resection: a national study. Ann Thorac Surg. 2022;113(4):1274–81.

    Article  PubMed  Google Scholar 

  29. Liu L, Zhang J, Wang G, Guo C, Chen Y, Huang C, et al. Delayed discharge after thoracic surgery under the guidance of ERAS protocols. Thorac Cardiovasc Surg. 2022;70(5):405–12.

    Article  PubMed  Google Scholar 

  30. Karunungan KL, Hadaya J, Tran Z, Sanaiha Y, Mandelbaum A, Revels SL, et al. Frailty is independently associated with worse outcomes after elective anatomic lung resection. Ann Thorac Surg. 2021;112(5):1639–46.

    Article  PubMed  Google Scholar 

  31. De León LE, Rochefort MM, Bravo-Iñiguez CE, Fox SW, Tarascio JN, Cardin K, et al. Opportunities for quality improvement in the morbidity pattern of older adults undergoing pulmonary lobectomy for cancer. J Geriatr Oncol. 2021;12(3):416–21.

    Article  PubMed  Google Scholar 

  32. Li S, Wang Z, Huang J, Fan J, Du H, Liu L, et al. Systematic review of prognostic roles of body mass index for patients undergoing lung cancer surgery: does the “obesity paradox” really exist? European J Cardiothorac Surg Off J European Assoc Cardiothorac Surg. 2017;51(5):817–28.

    Google Scholar 

  33. Towe CW, Thibault DP, Worrell SG, Bachman KC, Perry Y, Kosinski AS, et al. Factors associated with successful postoperative day one discharge after anatomic lung resection. Ann Thorac Surg. 2021;112(1):221–7.

    Article  PubMed  Google Scholar 

  34. McCully K, Leiper C, Sanders T, Griffin E. The effects of peripheral vascular disease on gait. J Gerontol A. 1999;54(7):B291–4.

    Article  CAS  Google Scholar 

  35. Batchelor TJP, Rasburn NJ, Abdelnour-Berchtold E, Brunelli A, Cerfolio RJ, Gonzalez M, et al. Guidelines for enhanced recovery after lung surgery: recommendations of the enhanced recovery after surgery (ERAS®) society and the European society of thoracic surgeons (ESTS). Eur J Cardiothorac Surg Off J Eur Assoc Cardiothorac Surg. 2019;55(1):91–115 (Epub 2018/10/12).

    Article  Google Scholar 

  36. Bao F, Dimitrovska NT, Hu S, Chu X, Li W. Safety of early discharge with a chest tube after pulmonary segmentectomy. Eur J Cardiothorac Surg. 2020;58(3):613–8.

    Article  PubMed  Google Scholar 

  37. Minervini F, Hanna WC, Brunelli A, Farrokhyar F, Miyazaki T, Bertolaccini L, et al. Outcomes of patients discharged home with a chest tube after lung resection: a multicentre cohort study. Canadian J Surg J Canadien de Chirurgie. 2022;65(1):E97–103.

    Article  Google Scholar 

  38. Murthy SC, Blackstone EH. Research based on big data: The good, the bad, and the ugly. J Thorac Cardiovasc Surg. 2016;151(3):629–30.

    Article  PubMed  Google Scholar 

  39. Rogers LJ, Bleetman D, Messenger DE, Joshi NA, Wood L, Rasburn NJ, et al. The impact of enhanced recovery after surgery (ERAS) protocol compliance on morbidity from resection for primary lung cancer. J Thorac Cardiovasc Surg. 2018;155(4):1843–52.

    Article  PubMed  Google Scholar 

  40. Kim BR, Sohn JY, Jang EJ, Jo J, Lee H, Ryu HG. Hospital case-volume and mortality after lung cancer surgery: a population-based retrospective cohort study. Lung Cancer. 2022;169:61–6.

    Article  PubMed  Google Scholar 

  41. Martins RS, Chang YH, Etzioni D, Stucky CC, Cronin P, Wasif N. Understanding variation in in-hospital mortality after major surgery in the United States. Annals of surgery. 2023.

Download references

Acknowledgements

None.

Funding

None.

Author information

Authors and Affiliations

  1. Division of Thoracic Surgery, Department of Surgery, Hackensack Meridian School of Medicine, Hackensack Meridian Health (HMH) Network–Central Region, 65 James Street, Edison, NJ, 08820, USA

    Russell Seth Martins, Kostantinos Poulikidis, M. Jawad Latif, Syed Shahzad Razi & Faiz Y. Bhora

  2. Medical College, Aga Khan University Hospital, Karachi, 74800, Pakistan

    Asad Saulat Fatimi & Amna Irfan Ansari

  3. Dow Medical College, Dow University of Health Sciences, Karachi, 74200, Pakistan

    Hamna Raheel

Authors
  1. Russell Seth Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asad Saulat Fatimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amna Irfan Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamna Raheel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kostantinos Poulikidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Jawad Latif
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Syed Shahzad Razi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Faiz Y. Bhora
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RSM, FB, KP, MJL, and SSR initially conceptualized and designed the study. RSM and ASF designed the search strings used to retrieve the articles. Two reviewers (HR and AIA) independently screened the initially retrieved articles, with conflicts being resolved by a third reviewer (ASF). Two independent reviewers (HR and AIA) conducted a full-text review of shortlisted articles, with disagreements being solved by a third reviewer (ASF). Two authors (AIA and HR) independently performed data extraction, with discrepancies being resolved by ASF and RSM. The meta-analysis was performed by ASF. Quality assessment of the studies and certainty of evidence assessment was performed by HR and AIA, with disagreements being resolved by a third reviewer (ASF). ASF and RSM prepared the supplementary material for the project. All authors contributed to writing, tabulation, and critically revising the manuscript and approving its final draft.

Corresponding author

Correspondence to Faiz Y. Bhora.

Ethics declarations

Ethical approval and consent to participate

This study was exempt from full ethical approval, as no original data was included. No human subjects were included in this study so no consent to participate had to be taken.

Consent for publication

Not applicable.

Competing interests

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Supplementary Material.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martins, R.S., Fatimi, A.S., Ansari, A.I. et al. Factors associated with safe and successful postoperative day 1 discharge after lung operations: a systematic review and meta-analysis. J Cardiothorac Surg 19, 91 (2024). https://doi.org/10.1186/s13019-024-02505-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13019-024-02505-4

Keywords

Journal of Cardiothoracic Surgery

ISSN: 1749-8090

Contact us