Assessment of whether in-hospital mortality for lobectomy is a useful standard for the quality of lung cancer surgery: retrospective study.
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ABSTRACT:
SUBMITTER: Treasure T
PROVIDER: S-EPMC164918 | biostudies-other | 2003 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundDespite implementation of enhanced recovery after surgery (ERAS) in lung surgery, potential barriers for improvements should be identified. The aim of this single-centre, prospective ERAS cohort study was to explore reasons for delayed patient discharge after video-assisted thoracoscopic surgery (VATS) lobectomy with a median length of hospital stay (LOS) of 2 days.MethodsConsecutive patients referred for VATS lobectomy were consulted twice daily by an investigator for the primary reasons for continued hospitalization. The secondary outcomes were risk factors for delayed recovery using univariate and multivariate regression analyses.ResultsA total of 147 patients were included (69 with LOS more than 2 days and 78 with LOS of 2 days or less) from April 2020 to December 2020. Air leak (27.7 per cent), pneumonia (20.2 per cent), pain (15.3 per cent), urinary/renal factors (11.0 per cent), atrial fibrillation (7.0 per cent), respiratory failure (4.5 per cent), cognitive factors/delirium (4.3 per cent), gastrointestinal factors (3.8 per cent), oxygen dependency (2.7 per cent), social factors (2.0 per cent), and pleural effusion (1.4 per cent) were important factors for discharge more than 2 days after surgery. The 30-day readmission rate after discharge was 21 per cent for LOS of 2 days or less and 22 per cent for LOS more than 2 days (P = 0.856). On a multivariate regression model, age (per 5-year increase, odds ratio (OR) 1.29, 95 per cent c.i. 1.01 to 1.66, P = 0.043) and forced expiratory volume in 1 s (FEV1) per cent (per 5 per cent increase, OR 0.89, 95 per cent c.i. 0.81 to 0.98, P = 0.021) were significantly related to discharge after more than 2 days.ConclusionDespite a short median LOS of 2 days, air leak, pneumonia, and pain remain the most important challenges for further improvement of the ERAS programme. Age and FEV1 per cent were statistically significant risk factors for LOS longer than 2 days.
| S-EPMC9070644 | biostudies-literature
Project description:To advance methods for the estimation of hospital performance based upon mortality ratios. Observational study estimating trust performance in a year derived according to comparative standards from a 3-year period, accounting for patient-level case-mix and overdispersion (unexplained variability). 23 363 630 admissions to the English National Health Service (NHS) by NHS Trust. Number of SDs (QUality and Outcomes Research Unit Measure, QUORUM banding) and comparative odds of hospital mortality difference from mean performance by trust compared for 2010/2011, 2008/2009 and 2009/2010, accounting for patient-level case-mix. The model was highly predictive of mortality (C statistic=0.93), and well calibrated by risk stratum. There was substantial overdispersion. No trusts were more than 3 SDs above the mean, and only one trust was more than 2 SDs above the mean for 2010/2011. QUORUM is highly predictive of patient mortality in hospital or up to 30 days after admission. However, like the Summary Hospital Mortality Indicator (SHMI), QUORUM is subjected to considerable remaining legitimate but unexplained variation. It is unlikely that measures like QUORUM and SHMI will be useful beyond identifying a very small number of trusts as potential outliers.
| S-EPMC3563139 | biostudies-literature
Project description:PURPOSE:Standards for surgical decisions are unavailable, hence treatment decisions can be personalized, but also introduce variation in treatment and outcome. National registrations seek to monitor healthcare quality. The goal of the study is to measure between-hospital variation in risk-standardized survival outcome after glioblastoma surgery and to explore the association between survival and hospital characteristics in conjunction with patient-related risk factors. METHODS:Data of 2,409 adults with first-time glioblastoma surgery at 14 hospitals were obtained from a comprehensive, prospective population-based Quality Registry Neuro Surgery in The Netherlands between 2011 and 2014. We compared the observed survival with patient-specific risk-standardized expected early (30-day) mortality and late (2-year) survival, based on age, performance, and treatment year. We analyzed funnel plots, logistic regression and proportional hazards models. RESULTS:Overall 30-day mortality was 5.2% and overall 2-year survival was 13.5%. Median survival varied between 4.8 and 14.9 months among hospitals, and biopsy percentages ranged between 16 and 73%. One hospital had lower than expected early mortality, and four hospitals had lower than expected late survival. Higher case volume was related with lower early mortality (P?=?0.031). Patient-related risk factors (lower age; better performance; more recent years of treatment) were significantly associated with longer overall survival. Of the hospital characteristics, longer overall survival was associated with lower biopsy percentage (HR 2.09, 1.34-3.26, P?=?0.001), and not with academic setting, nor with case volume. CONCLUSIONS:Hospitals vary more in late survival than early mortality after glioblastoma surgery. Widely varying biopsy percentages indicate treatment variation. Patient-related factors have a stronger association with overall survival than hospital-related factors.
| S-EPMC6700042 | biostudies-literature
Project description:ObjectivesTo assess the association between mortality and the day of elective surgical procedure.DesignRetrospective analysis of national hospital administrative data.SettingAll acute and specialist English hospitals carrying out elective surgery over three financial years, from 2008-09 to 2010-11.ParticipantsPatients undergoing elective surgery in English public hospitals.Main outcome measureDeath in or out of hospital within 30 days of the procedure.ResultsThere were 27,582 deaths within 30 days after 4,133,346 inpatient admissions for elective operating room procedures (overall crude mortality rate 6.7 per 1000). The number of weekday and weekend procedures decreased over the three years (by 4.5% and 26.8%, respectively). The adjusted odds of death were 44% and 82% higher, respectively, if the procedures were carried out on Friday (odds ratio 1.44, 95% confidence interval 1.39 to 1.50) or a weekend (1.82, 1.71 to 1.94) compared with Monday.ConclusionsThe study suggests a higher risk of death for patients who have elective surgical procedures carried out later in the working week and at the weekend.
| S-EPMC3665889 | biostudies-literature
Project description:BackgroundLung cancer is highly prevalent in Chinese population. The association of operative approach with economic burden in these patients remains unknown.ObjectivesThis institution-level cohort study aimed to compare the cost-related clinical outcomes and health care costs among patients undergoing video-assisted thoracoscopic surgery (VATS) and open lobectomy, and to investigate the factors associated with the costs.MethodsThis retrospective cohort study included patients who underwent VATS or open lobectomy in a provincial referral cancer center in China in 2018. Propensity score matching (PSM) method was applied to balance the baseline characteristics in VATS lobectomy and open lobectomy group. Clinical effectiveness measures included post-operative blood transfusion, lung infection, and length of stay (LOS). Hospitalization costs were extracted from hospital information system to assess economic burden. Multivariable generalized linear model (GLM) with gamma probability distribution and log-link was used to analyze the factors associated with total costs.ResultsAfter PSM, 376 patients were selected in the analytic sample. Compared to open lobectomy group, the VATS lobectomy group had a lower blood transfusion rate (2.13% vs. 3.19%, P = 0.75), lower lung infection rate (21.28% vs. 39.89%, P < 0.001) and shorter post-operative LOS (9.4 ± 3.22 days vs. 10.86 ± 4.69 days, P < 0.001). Total hospitalization costs of VATS lobectomy group and open lobectomy were similar: Renminbi (RMB) 84398.03 ± 13616.13, RMB 81,964.92 ± 16748.11, respectively (P = 0.12). Total non-surgery costs were significantly lower in the VATS lobectomy group than in the open lobectomy group: RMB 41948.40 ± 7747.54 vs. RMB 45752.36 ± 10346.42 (P < 0.001). VATS approach, lung infection, longer post-operative length of stay, health insurance coverage, and lung cancer diagnosis were associated with higher total hospitalization costs (P < 0.05).ConclusionsVATS lobectomy has a lower lung infection rate, and shorter post-operative LOS than open lobectomy. Future studies are needed to investigate other aspects of clinical effectiveness and the economic burden from a societal perspective.
| S-EPMC8400899 | biostudies-literature
Project description:BackgroundAlthough an increasing number of pregnant women in resource-limited areas deliver in health-care facilities, maternal mortality remains high in these settings. Inadequate diagnosis and management of common life-threatening conditions is an important determinant of maternal mortality. We analysed the clinicopathological discrepancies in a series of maternal deaths from Mozambique and assessed changes over 10 years in the diagnostic process. We aimed to provide data on clinical diagnostic accuracy to be used for improving quality of care and reducing maternal mortality.MethodsWe did a retrospective analysis of clinicopathological discrepancies in 91 maternal deaths occurring from Nov 1, 2013, to March 31, 2015 (17 month-long period), at a tertiary-level hospital in Mozambique, using complete diagnostic autopsies as the gold standard to ascertain cause of death. We estimated the performance of the clinical diagnosis and classified clinicopathological discrepancies as major and minor errors. We compared the findings of this analysis with those of a similar study done in the same setting 10 years earlier.FindingsWe identified a clinicopathological discrepancy in 35 (38%) of 91 women. All diagnostic errors observed were classified as major discrepancies. The sensitivity of the clinical diagnosis for puerperal infections was 17% and the positive predictive value was 50%. The sensitivity for non-obstetric infections was 48%. The sensitivity for eclampsia was 100% but the positive predictive value was 33%. Over the 10-year period, the performance of clinical diagnosis did not improve, and worsened for some diagnoses, such as puerperal infection.InterpretationDecreasing maternal mortality requires improvement of the pre-mortem diagnostic process and avoidance of clinical errors by refining clinical skills and increasing the availability and quality of diagnostic tests. Comparison of post-mortem information with clinical diagnosis will help monitor the reduction of clinical errors and thus improve the quality of care.FundingBill & Melinda Gates Foundation and Instituto de Salud Carlos III.
| S-EPMC7303952 | biostudies-literature
Project description:To assess the value of a novel composite measure for identifying the best hospitals for major procedures.We used national Medicare data for patients undergoing five high-risk surgical procedures between 2005 and 2008.For each procedure, we used empirical Bayes techniques to create a composite measure combining hospital volume, risk-adjusted mortality with the procedure of interest, risk-adjusted mortality with other related procedures, and other variables. Hospitals were ranked based on 2005-2006 data and placed in one of three groups: 1-star (bottom 20 percent), 2-star (middle 60 percent), and 3-star (top 20 percent). We assessed how well these ratings forecasted risk-adjusted mortality rates in the next 2 years (2007-2008), compared to other measures.For all five procedures, the composite measures based on 2005-2006 data performed well in predicting future hospital performance. Compared to 1-star hospitals, risk-adjusted mortality was much lower at 3-star hospitals for esophagectomy (6.7 versus 14.4 percent), pancreatectomy (4.7 versus 9.2 percent), coronary artery bypass surgery (2.6 versus 5.0 percent), aortic valve replacement (4.5 versus 8.5 percent), and percutaneous coronary interventions (2.4 versus 4.1 percent). Compared to individual surgical quality measures, the composite measures were better at forecasting future risk-adjusted mortality. These measures also outperformed the Center for Medicare and Medicaid Services (CMS) Hospital Compare ratings.Composite measures of surgical quality are very effective at predicting hospital mortality rates with major procedures. Such measures would be more informative than existing quality indicators in helping patients and payers identify high-quality hospitals with specific procedures.
| S-EPMC3448279 | biostudies-literature
Project description:ImportanceHospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.ObjectiveTo identify the type of score that best predicts hospital readmissions.Design, setting, and participantsThis prognostic study included 14?062 consecutive adult hospital patients with 16?649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions.Main outcomes and measuresThe 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score.ResultsOf the 10?732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P?<?.001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P?<?.001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients.Conclusions and relevanceAmong 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.
| S-EPMC6484642 | biostudies-literature
Project description:Background: Preoperative detection of high-/low-risk postoperative ventricular arrhythmia (POVA) patients using a noninvasive method is an important issue in the clinical setting. This study mainly aimed to determine the usefulness of several preoperative electrocardiographic (ECG) markers in the risk assessment of POVA with cardiac surgery. Method: We enrolled 1024 consecutive patients undergoing cardiac surgery, and a total of 823 patients were included in the study. Logistic regression analysis determined preoperative ECG markers. A new risk predicting model were developed to predict occurrence of POVA, and the receiver operating characteristic curve (ROC) was used to validate this model. Results: Of these, 337 patients experienced POVA, and 485 patients did not experience POVA in this retrospective study. Among 15 ECG markers, a univariate analysis found a strong association between POVA and preoperative VA, the R-wave in lead aVR, the QRS wave, index of cardiac electrophysiological balance (iCEB), QT interval corrected (QTc), Tpeak-Tend interval (Tpe) in lead V2, the J wave in the inferolateral leads, pathological Q wave, and SV1+RV5>35 mm. Multivariate analysis showed that a preoperative J wave [adjusted odds ratio (AOR): 3.80; 95% CI: 1.88-7.66; p < 0.001], Tpe >112.5-ms (AOR: 2.80; 95% CI: 1.57-4.99; p < 0.001), and SV1+RV5 >35 mm (AOR: 2.92; 95% CI: 1.29-6.60; p = 0.01) were independently associated with POVA. A new risk predicting model were developed in predicting POVA. Conclusion: The ECG biomarkers including J wave, Tpe >112.5 ms, and SV1+RV5 >35 mm were significantly predicted POVAs. A risk predicting model developed with electrocardiographic risk markers preoperatively predicted POVAs.
| S-EPMC9108335 | biostudies-literature
Project description:IntroductionWe sought to define the individual contributions of patient characteristics (PCs), hospital characteristics (HCs), case volume (CV), and social determinants of health (SDoH) on in-hospital mortality (IHM) after complex cancer surgery.MethodsThe California Department of Health Care Access and Information database identified patients who underwent esophagectomy (ES), pneumonectomy (PN), pancreatectomy (PD), or proctectomy (PR) for a malignant diagnosis between 2010 and 2020. Multi-level multivariable regression was performed to assess the proportion of variance explained by PCs, HCs, CV and SDoH on IHM.ResultsA total of 52,838 patients underwent cancer surgery (ES: n = 2,700, 5.1%; PN: n = 30,822, 58.3%; PD: n = 7530, 14.3%; PR: n = 11,786, 22.3%) across 294 hospitals. The IHM for the overall cohort was 1.7% and varied from 4.4% for ES to 0.8% for PR. On multivariable regression, PCs contributed the most to the variance in IHM (overall: 32.0%; ES: 21.6%; PN: 28.0%; PD: 20.3%; PR: 39.9%). Among the overall cohort, CV contributed 2.4%, HCs contributed 1.3%, and SDoH contributed 1.2% to the variation in IHM. CV was the second highest contributor to IHM among ES (5.3%), PN (5.3%), and PD (5.9%); however, HCs were a more important contributor among patients who underwent PR (8.0%). The unexplained variance in IHM was highest among ES (72.4%), followed by the PD (67.5%) and PN (64.6%) patient groups.ConclusionsPCs are the greatest underlying contributor to variations in IHM following cancer surgery. These data highlight the need to focus on optimizing patients and exploring unexplained sources of IHM to improve quality of surgical care.
| S-EPMC10997543 | biostudies-literature