Preoperative Opioid Prescribers and Lumbar Fusion: Their Effect on Clinical Outcomes and Postoperative Opioid Usage.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To determine the impact of multiple preoperative opioid prescribers on postoperative patient opioid usage and patient-reported outcome measures after single-level lumbar fusion. SUMMARY OF BACKGROUND DATA: Prior literature has identified opioid prescriptions from multiple postoperative providers increase opioid usage rates. However, there is limited evidence on how multiple preoperative opioid prescribers affect postoperative opioid usage or clinical outcomes after a single-level lumbar fusion. PATIENTS AND METHODS: A retrospective review of single-level transforaminal lumbar interbody fusion or posterolateral lumbar fusions between September 2017 and February 2020 at a single academic institution was performed. Patients were excluded if they were not identifiable in our state's prescription drug-monitoring program. Univariate comparisons and regression analyses identified factors associated with postoperative clinical outcomes and opioid usage. RESULTS: Of 239 patients, 160 (66.9%) had one or fewer preoperative prescribers and 79 (33.1%) had >1 prescribers. On regression analysis, the presence of multiple preoperative prescribers was an independent predictor of increased improvement in Visual Analog Scale (∆VAS) Back (ß=-1.61, P =0.012) and the involvement of a nonoperative spine provider was an independent predictor of increased improvement in ∆VAS Leg (ß = -1.53, P = 0.034). Multiple preoperative opioid prescribers correlated with an increase in opioid prescriptions postoperatively (ß = 0.26, P = 0.014), but it did not significantly affect the amount of morphine milligram equivalents prescribed (ß = -48.79, P = 0.146). A greater number of preoperative opioid prescriptions predicted worse improvements in VAS Back, VAS Leg, and Oswestry Disability Index and predicted increased postoperative opioid prescriptions, prescribers, and morphine milligram equivalents. CONCLUSIONS: Multiple preoperative opioid prescribers predicted increased improvement in postoperative back pain, whereas preoperative involvement of a nonoperative spine provider predicted improvements in leg pain after surgery. The number of preoperative opioid prescriptions was a better metric for predicting poor postoperative outcomes and increased opioid consumption compared with the number of preoperative opioid prescribers.

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its' determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan.

Land use/land cover (LULC) changes due to urban growth on the regional scale affect land surface temperature (LST). The present study aims to assess the LULC changes and their impact on LST over Mardan and Charsadda districts of Khyber Pakhtunkhwa (KP), Pakistan, in the period from 1990 to 2019. Landsat satellite (TM& ETM +) datasets in the period from 1990 to 2010 and Sentinel-2 images from 2016 to 2019 were used in this study. All the datasets were pre-processed and the LULC types were classified by maximum likelihood classification algorithm. The vegetation degradation was computed from normalized difference vegetation index (NDVI), and the LST was derived based on the LULC changes. The results showed that the overall accuracy of LULC classification was 87.84%. Dramatic LULC changes were observed during the last three decades, where the vegetation degradation area was decreased from 1307.8 (59.27%) to 1147.6 km2 (52.1%) and the barren land area increased from 816.6 (37.07%) to 961.4 km2 (42.64%). Similarly, the built-up area has also increased from 57.2 (2.5%) to 104.3 km2 (4.73%) in the years 1990 and 2019, respectively. These variations in LULC types have significantly influenced the LST from 1990 to 2019; specifically, the LST of built-up area, barren land, and vegetation cover increased from 20.1 to 32.1 °C, 21.5 to 35.5 °C, and 17.1 to 28.2 °C, respectively. The regression line plotted defines that the LST has a negative correlation with NDVI and a positive correlation with normalized difference of built-up index (NDBI). In particular, the vegetation and land covers dramatically transformed to barren land and/or to urban development over the study area in the period from 1990 to2019, which has severely affected the LST and the natural resources of the study area. Therefore, our study will be very helpful for managing the rapid environmental changes and urban planning.