An application of adaptive cluster sampling for estimating total suspended sediment load.

Suspended sediment transport in river for a particular period is a timescale finite population. This population shows natural aggregation tendencies in sediment concentration particularly during floods. Adaptive cluster sampling (ACS) can be potentially conducted for sampling from this rare clustered population and estimating total load. To illustrate the performance of ACS in sediment estimation, a comparative study was carried out in the Gorgan-Rood River, Iran, with around a 5 year daily concentration record. The total sediment loads estimated by ACS were statistically compared to the observed load, estimations of selection at list time (SALT) and conventional sediment rating curve with and without correction factors. The results suggest that none of the sediment rating curves produced accurate estimates, while both ACS and SALT showed satisfactory results at a semi-weekly sampling frequency. The best estimation obtained by the rating curves did not show a percent error better than -40%; however, ACS and SALT underestimated the load at less than 5%. The results of this study suggest ACS could improve river monitoring programs

Effect of adaptive cluster sampling design on accuracy of sediment rating curve estimation.

Adaptive cluster sampling represents a design whereby more samples during high storm hydrographs can be obtained in the field compared to the commonly used calendar-based. To compare the effect of these sampling designs on accuracy of sediment rating curve estimation, we performed a study for Gorgan-Rood River, Iran by synthesizing sample sets from daily records. The load estimates obtained by conventional, two bias-corrected, and logged mean load within discharge classes (LMLWDCs) rating curves were statistically evaluated. All rating curves derived from calendar-based sample sets–except those with a nonparametric correction factor–underestimated the average load from 25 to 76%. Rating curves derived from adaptive sample sets, however, increased the sediment load to as much as 30%. Among this group of rating curves, LMLWDC produced the most accurate results with only 3% overestimation and a coefficient of variation in the order of 14% when the sampling frequency was semiweekly. The more accurate estimates from adaptive sample sets are likely due to the inclusion of more samples from high load periods

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society

Adaptive cluster sampling for a temporal-scale population

Adaptive cluster sampling (ACS) is appropriate for rare clustered populations with localization tendencies. Up to now, it has been used exclusively for investigating spatial-scale problems rather than temporal-scale such as t his study is dealing with, i.e.sediment transport in rivers. Suspended sediment load is carried mostly during relatively short periods coincide with high flows otherwise negligible. In ACS, more samples from critical river stages can be taken with respect to the aggregation tendencies of sediment loads during transport; thus increasing the level of representativeness of samples. Adoption of ACS to this new area needs further verification and adaptation such as definition of the sampling unit, population frame, neighborhood relation, and threshold. In this study, several scenarios were defined for the purpose of evaluating the ACS in sediment estimation. Numerous sample sets were taken from intensive discharge-load records of Sg. Pangsun River, Malaysia. These sample sets are different with respect to initial sample size, neighborhood relation, and discharge threshold. Total suspended sediment loads were then estimated using modified Horvitz-Thompson method. The comparison made between the symmetric neighborhood relation and the forward method suggested in this study showed that the latter could be used instead of the former in sediment studies without losing the accuracy. The findings also suggested the flow duration curve is a useful tool for ranking initial samples in order to determine an optimum discharge threshold