Climate change as an unexpected co-factor promoting coral eating seastar (Acanthaster planci) outbreaks

Coral reefs face a crisis due to local and global anthropogenic stressors. A large proportion of the ~50% coral loss on the Great Barrier Reef has been attributed to outbreaks of the crown-of-thorns-seastar (COTS). A widely assumed cause of primary COTS outbreaks is increased larval survivorship due to higher food availability, linked with anthropogenic runoff . Our experiment using a range of algal food concentrations at three temperatures representing present day average and predicted future increases, demonstrated a strong influence of food concentration on development is modulated by temperature. A 2°C increase in temperature led to a 4.2–4.9 times (at Day 10) or 1.2–1.8 times (Day 17) increase in late development larvae. A model indicated that food was the main driver, but that temperature was an important modulator of development. For instance, at 5000 cells ml−1 food, a 2°C increase may shorten developmental time by 30% and may increase the probability of survival by 240%. The main contribution of temperature is to ‘push’ well-fed larvae faster to settlement. We conclude that warmer sea temperature is an important co-factor promoting COTS outbreaks.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

OBJECTIVES: To derive and validate a mortality prediction model from information available at ED triage. METHODS: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. RESULTS: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. CONCLUSIONS: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

Toward a standard approach to measurement and reporting of perioperative mortality rate as a global indicator for surgery

INTRODUCTION: The proportion of patients who die during or after surgery, otherwise known as the perioperative mortality rate (POMR), is a credible indicator of the safety and quality of operative care. Its accuracy and usefulness as a metric, however, particularly one that enables valid comparisons over time or between jurisdictions, has been limited by lack of a standardized approach to measurement and calculation, poor understanding of when in relation to surgery it is best measured, and whether risk-adjustment is needed. Our aim was to evaluate the value of POMR as a global surgery metric by addressing these issues using 4, large, mixed, surgical datasets that represent high-, middle-, and low-income countries. METHODS: We obtained data from the New Zealand National Minimum Dataset, the Geelong Hospital patient management system in Australia, and purpose-built surgical databases in Pietermaritzburg, South Africa, and Port Moresby, Papua New Guinea. For each site, we calculated the POMR overall as well as for nonemergency and emergency admissions. We assessed the effect of admission episodes and procedures as the denominator and the difference between in-hospital POMR and POMR, including postdischarge deaths up to 30 days. To determine the need for risk-adjustment for age and admission urgency, we used univariate and multivariate logistic regression to assess the effect on relative POMR for each site. RESULTS: A total of 1,362,635 patient admissions involving 1,514,242 procedures were included. More than 60% of admissions in Pietermaritzburg and Port Moresby were emergencies, compared with less than 30% in New Zealand and Geelong. Also, Pietermaritzburg and Port Moresby had much younger patient populations (P < .001). A total of 8,655 deaths were recorded within 30 days, and 8-20% of in-hospital deaths occurred on the same day as the first operation. In-hospital POMR ranged approximately 9-fold, from 0.38 per 100 admissions in New Zealand to 3.44 per 100 admissions in Pietermaritzburg. In New Zealand, in-hospital 30-day POMR underestimated total 30-day POMR by approximately one third. The difference in POMR if procedures were used instead of admission episodes ranged from 7 to 70%, although this difference was less when central line and pacemaker insertions were excluded. Age older than 65 years and emergency admission had large, independent effects on POMR but relatively little effect in multivariate analysis on the relative odds of in-hospital death at each site. CONCLUSION: It is possible to collect POMR in countries at all level of development. Although age and admission urgency are strong, independent associations with POMR, a substantial amount of its variance is site-specific and may reflect the safety of operative and anesthetic facilities and processes. Risk-adjustment is desirable but not essential for monitoring system performance. POMR varies depending on the choice of denominator, and in-hospital deaths appear to underestimate 30-day mortality by up to one third. Standardized approaches to reporting and analysis will strengthen the validity of POMR as the principal indicator of the safety of surgery and anesthesia care.

Colorectal preOperative surgical score (CrOSS) for mortality in major colorectal surgery

BACKGROUND: Colorectal surgery carries a significant mortality risk, with reported rates of 1-6% for elective surgery and up to 22% in the emergency setting. Both clinicians and patients will benefit from being able to predict the likelihood of death before surgery. Recently, we have described and validated two risk stratification models for colorectal surgery, the Barwon Health 2012 and Association Française de Chirurgie models. However, these models are not suitable for assessment at patient's bedside. The purpose of this study is to develop a simplified preoperative model capable of predicting mortality following colorectal surgery. METHODS: The new model is termed Colorectal preOperative Surgical Score (CrOSS). The development and internal validation of CrOSS was performed using a prospectively maintained colorectal database. External validation was performed using retrospective data. Univariate and multivariate analyses were performed in model development. Calibration and discrimination were used for model validation. RESULTS: There were 474 and 389 consecutive colorectal surgeries at Geelong Hospital and Western Hospital. Overall mortality rates were 5.16% and 1.03%, respectively. Significant predictors for mortality were as follows: age ≥70, urgent operation, albumin ≤30 g/L and congestive heart failure (receiver operating characteristic (ROC) = 0.870, calibration P-value = 0.937). The predicted risk of mortality was stratified according to the risk profile of 0.39-66.51%. When validated externally, CrOSS predicted mortality accurately (ROC = 0.847, calibration P-value = 0.199). CONCLUSIONS: A robust and simple preoperative model has been created to risk-stratify patients for colorectal surgery. This was successfully validated at another tertiary hospital.

Monitoring and evaluating surgical care: defining perioperative mortality rate and standardising data collection

BACKGROUND: Case volume per 100 000 population and perioperative mortality rate (POMR) are key indicators to monitor and strengthen surgical services. However, comparisons of POMR have been restricted by absence of standardised approaches to when it is measured, the ideal denominator, need for risk adjustment, and whether data are available. We aimed to address these issues and recommend a minimum dataset by analysing four large mixed surgical datasets, two from well-resourced settings with sophisticated electronic patient information systems and two from resource-limited settings where clinicians maintain locally developed databases. METHODS: We obtained data from the New Zealand (NZ) National Minimum Dataset, the Geelong Hospital patient management system in Australia, and purpose-built surgical databases in Pietermaritzburg, South Africa (PMZ) and Port Moresby, Papua New Guinea (PNG). Information was sought on inclusion and exclusion criteria, coding criteria, and completeness of patient identifiers, admission, procedure, discharge and death dates, operation details, urgency of admission, and American Society of Anesthesiologists (ASA) score. Date-related errors were defined as missing dates and impossible discrepancies. For every site, we then calculated the POMR, the effect of admission episodes or procedures as denominator, and the difference between in-hospital POMR and 30-day POMR. To determine the need for risk adjustment, we used univariate and multivariate logistic regression to assess the effect on relative POMR for each site of age, admission urgency, ASA score, and procedure type. FINDINGS: 1 365 773 patient admissions involving 1 514 242 procedures were included, among which 8655 deaths were recorded within 30 days. Database inclusion and exclusion criteria differed substantially. NZ and Geelong records had less than 0·1% date-related errors and greater than 99·9% completeness. PMZ databases had 99·9% or greater completeness of all data except date-related items (94·0%). PNG had 99·9% or greater completeness for date of birth or age and admission date and operative procedure, but 80-83% completeness of patient identifiers and date related items. Coding of procedures was not standardised, and only NZ recorded ASA status and complete post-discharge mortality. In-hospital POMR range was 0·38% in NZ to 3·44% in PMZ, and in NZ it underestimated 30-day POMR by roughly a third. The difference in POMR by procedures instead of admission episodes as denominator ranged from 10% to 70%. Age older than 65 years and emergency admission had large independent effects on POMR, but relatively little effect in multivariate analysis on the relative odds of in-hospital death at each site. INTERPRETATION: Hospitals can collect and provide data for case volume and POMR without sophisticated electronic information systems. POMR should initially be defined by in-hospital mortality because post-discharge deaths are not usually recorded, and with procedures as denominator because details allowing linkage of several operations within one patient's admission are not always present. Although age and admission urgency are independently associated with POMR, and ASA and case mix were not included, risk adjustment might not be essential because the relative odds between sites persisted. Standardisation of inclusion criteria and definitions is needed, as is attention to accuracy and completeness of dates of procedures, discharge and death. A one-page, paper-based form, or alternatively a simple electronic data collection form, containing a minimum dataset commenced in the operating theatre could facilitate this process. FUNDING: None.

Invasive infections due to filamentous fungi other than Aspergillus: epidemiology and determinants of mortality

The epidemiology of invasive fungal disease (IFD) due to filamentous fungi other than Aspergillus may be changing. We analysed clinical, microbiological and outcome data in Australian patients to determine the predisposing factors and identify determinants of mortality. Proven and probable non-Aspergillus mould infections (defined according to modified European Organization for Research and Treatment of Cancer/Mycoses Study Group criteria) from 2004 to 2012 were evaluated in a multicentre study. Variables associated with infection and mortality were determined. Of 162 episodes of non-Aspergillus IFD, 145 (89.5%) were proven infections and 17 (10.5%) were probable infections. The pathogens included 29 fungal species/species complexes; mucormycetes (45.7%) and Scedosporium species (33.3%) were most common. The commonest comorbidities were haematological malignancies (HMs) (46.3%) diabetes mellitus (23.5%), and chronic pulmonary disease (16%); antecedent trauma was present in 21% of cases. Twenty-five (15.4%) patients had no immunocompromised status or comorbidity, and were more likely to have acquired infection following major trauma (p <0.01); 61 (37.7%) of cases affected patients without HMs or transplantation. Antifungal therapy was administered to 93.2% of patients (median 68 days, interquartile range 19-275), and adjunctive surgery was performed in 58.6%. The all-cause 90-day mortality was 44.4%; HMs and intensive-care admission were the strongest predictors of death (both p <0.001). Survival varied by fungal group, with the risk of death being significantly lower in patients with dematiaceous mould infections than in patients with other non-Aspergillus mould infections. Non-Aspergillus IFD affected diverse patient groups, including non-immunocompromised hosts and those outside traditional risk groups; therefore, definitions of IFD in these patients are required. Given the high mortality, increased recognition of infections and accurate identification of the causative agent are required.