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

A Subtractive Feedforward Controller Based on Symmetrical Components Decomposition for DFIG Under Balanced and Unbalanced Loads in Weak Grids

© 2016, King Fahd University of Petroleum & Minerals. Supplying unbalanced load by a doubly fed induction generator (DFIG) causes power and torque pulsation due to its unbalanced stator and rotor currents. Researchers showed that power and torque pulsation increase the rate of the machine’s tear-and-wear and decrease its life time. This becomes stringier in a weak grid where the DFIG supplies a substantial amount of the load’s power. In order to protect the machine, the torque and power pulsation must be minimized. This paper proposes a subtractive feedforward controller that minimizes the DFIG’s torque and power pulsation of the DFIG machine by forcing the machine to supply the load with balanced current. The remaining unbalanced portion of the current is supplied by the grid. The proposed controller is made up of two main steps: First, we used our symmetrical components decomposition technique to generate dc positive and negative sequences in dq synchronous reference frame without the use of bandpass filters to filter out the 2 ω s components. In the second step, a subtractive feedforward compensator for the negative sequence is added to the generic controller. The compensator removes the negative sequence from the stator currents by adjusting the PWM mechanism at the rotor side. Agreeing with other researchers, the zero sequence was found to be absent in standard internally ungrounded DFIG machines; hence, a zero-sequence compensator was not needed. Our simulation results showed that our proposed controller works very well with balanced as well as the unbalanced loads. Under unbalanced conditions, our simulations showed that the negative sequence stator currents were diminished. This forces the supplied DFIG currents the load to be balanced, which leads to minimize torque and power pulsation. The proposed controller is also proven to be effective by extracting the maximum power under balanced as well as unbalanced loads