Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Four genotyping schemes for phylogenetic analysis of Pseudomonas aeruginosa: comparison of their congruence with multi-locus sequence typing.

Several molecular typing schemes have been proposed to differentiate among isolates and clonal groups, and hence establish epidemiological or phylogenetic links. It has been widely accepted that multi-locus sequence typing (MLST) is the gold standard for phylogenetic typing/long-term epidemiological surveillance, but other recently described methods may be easier to carry out, especially in settings with limited access to DNA sequencing. Comparing the performance of such techniques to MLST is therefore of relevance. A study was therefore carried out with a collection of P. aeruginosa strains (n = 133) typed by four typing schemes: MLST, multiple-locus variable number tandem repeat analysis (MLVA), pulsed-field gel electrophoresis (PFGE) and the commercial DiversiLab microbial typing system (DL). The aim of this study was to compare the results of each typing method with MLST. The Simpson's indices of diversity were 0.989, 0.980, 0.961 and 0.906 respectively for PFGE, MLVA, DL and MLST. The congruence between techniques was measured by the adjusted Wallace index (W): this coefficient indicates the probability that a pair of isolates which is assigned to the same type by one typing method is also typed as identical by the other. In this context, the congruence between techniques was recorded as follow: MLVA-type to predict MLST-type (93%), PFGE to MLST (92%), DL to MLST (64.2%), PFGE to MLVA (63.5%) and PFGE to DL (61.7%). Conversely, for all above combinations, prediction was very poor. The congruence was increased at the clonal complex (CC) level. MLST is regarded the gold standard for phylogenetic classification of bacteria, but is rather laborious to carry out in many settings. Our data suggest that MLVA can predict the MLST-type with high accuracy, and even higher when studying the clonal complex level. Of the studied three techniques MLVA was therefore the best surrogate method to predict MLST

Number of genotypes, clonal complexes and singletons retrieved from the four genotyping schemes.

<p>Number of genotypes, clonal complexes and singletons retrieved from the four genotyping schemes.</p

Congruence between typing methods determined by the Adjusted Wallace coefficient [95% CI].

<p>CI, confidence interval.</p><p>MLST-CC, MLST clonal complex.</p><p>MLVA-CC, MLVA clonal complex.</p

Simpson's index of diversity of the different typing methods.

<p>SID, Simpson's index of diversity.</p><p>CI, confidence interval.</p

Graphical representation of 44 isolates belonging to MLST-CC235 analyzed by PFGE, DL, MLST and MLVA.

<p>A; Dendrogram generated by PFGE banding pattern, the clustering was done by UPGMA using the Dice coefficient with a tolerance position of 1%. 19 PFGE types were obtained with a cut-off of 80% of similarity. B; Dendrogram generated by the Pearson cluster analysis of rep-PCR results. PFGE-type, ST, MT and virtual gel are shown for each isolate. STs are depicted in colored boxes. A similarity index cut-off value of 95% was used by the DiversiLab® software to define genetic classification and to highlight the 9 DL-types. C; Minimum Spanning Tree illustrating the clonal complex MLST-CC235, the predominant ST235 is the primary founder surrounded by its SLVs (STs 976, 230, 989 and 227). Each color was assigned to each distinct ST. D; Minimum Spanning Tree of MLST-CC235 isolates typed by MLVA, Each circle represents an MT and 13 different colors were assigned to each distinct MT.</p

Minimum Spanning trees (MSTs) of 133 <i>P. aeruginosa</i> isolates based on MLVA, MLST, PFGE and DiversiLab data.

<p>Each network represents its own genetic relatedness among isolates and displays its relative concordance against MLVA-CC and MLST-CC. A; Clustering of MLVA profile was done using the categorical coefficient. Each circle represents an MT, the size of which indicates the number of isolates with this particular type. Black lines connecting pairs of MTs indicate that they differ in one VNTR locus (thick lines), two VNTR locus (thin), or three to 15 VNTR locus (dashed). Grey zones surround MTs that belong to the same MLVA-CC. B; Clustering was done using MLST character data. Each circle represents an ST, the size of each circle indicates the number of isolates with this type. Black lines connecting pairs of STs indicate that they differ in one allele locus (thick lines), two alleles locus (thin), or three to seven (dashed). Grey zones surround STs that belong to the same MLST-CC. C; MST with permutation resampling with majority summary based on band matching class, each single node represents a distinct PFGE-type, the collapsed nodes represent closely related patterns with ≤6 band class differences. D; MST with permutation resampling with majority summary based on band matching class, each single node represents a distinct DL pattern, the collapsed nodes represent closely related patterns with ≤2 band class differences.</p

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

Background: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit