Perioperative structure and process quality and safety indicators: a systematic review

BACKGROUND: Clinical indicators assess healthcare structures, processes, and outcomes. While used widely, the exact number and level of scientific evidence of these indicators remains unclear. The aim of this study was to evaluate the number, type, and evidence base of clinical process and structure indicators currently available for quality and safety measurement in perioperative care. METHODS: We performed a systematic review searching Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Google Scholar, and System for Information in Grey Literature in Europe databases for English language human studies in adults (age >18) published in the past 10 years (January 2005–January 2016). We also included professional and governmental body publications and guidelines describing the development, validation, and use of structure and process indicators in perioperative care. RESULTS: We identified 43 860 journal articles and 43 relevant indicator program publications. From these, we identified a total of 1282 clinical indicators, split into structure (36%, n=463) and process indicators (64%, n=819). The dimensions of quality most frequently addressed were effectiveness (38%, n=475) and patient safety (29%, n=363). The majority of indicators (53%, n=675) did not have a level of evidence ascribed in their literature. Patient-centred metrics accounted for the fewest published clinical indicators. CONCLUSIONS: Despite widespread use, the majority of clinical indicators are not based on a strong level of scientific evidence. There may be scope in setting standards for the development and validation process of clinical indicators. Most indicators focus on the effectiveness, safety, and efficiency of care

Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre-emptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Peri-operative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulin-dependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 pre-operative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20–30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain

Perioperative structure and process quality and safety indicators: a systematic review

Background: Clinical indicators assess healthcare structures, processes, and outcomes. While used widely, the exact number and level of scientific evidence of these indicators remains unclear. The aim of this study was to evaluate the number, type, and evidence base of clinical process and structure indicators currently available for quality and safety measurement in perioperative care. Methods: We performed a systematic review searching Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Google Scholar, and System for Information in Grey Literature in Europe databases for English language human studies in adults (age &gt;18) published in the past 10 years (January 2005–January 2016). We also included professional and governmental body publications and guidelines describing the development, validation, and use of structure and process indicators in perioperative care. Results: We identified 43 860 journal articles and 43 relevant indicator program publications. From these, we identified a total of 1282 clinical indicators, split into structure (36%, n=463) and process indicators (64%, n=819). The dimensions of quality most frequently addressed were effectiveness (38%, n=475) and patient safety (29%, n=363). The majority of indicators (53%, n=675) did not have a level of evidence ascribed in their literature. Patient-centred metrics accounted for the fewest published clinical indicators. Conclusions: Despite widespread use, the majority of clinical indicators are not based on a strong level of scientific evidence. There may be scope in setting standards for the development and validation process of clinical indicators. Most indicators focus on the effectiveness, safety, and efficiency of care. PROSPERO database: CRD4201501277.</p

A new class of third-order nonlinear optical materials: laser pulse-duration dependant saturable absorption and nonlinear refraction in platinum(ii) diimine-dithiolate complexes

The third-order nonlinear optical (NLO) properties of a series of platinum diimine-dithiolate complexes [Pt(N^N)(S^S)] were investigated by means of Z-scan measurements, revealing second hyperpolarizability values up to 10−29 esu, saturable absorption properties, and nonlinear refractive behaviour, which were rationalized also by means of DFT calculations