A Preoperative Risk Assessment Tool for Predicting Adverse Outcomes among Total Shoulder Arthroplasty Patients.

BACKGROUND: With the increased utilization of Total Shoulder Arthroplasty (TSA) in the outpatient setting, understanding the risk factors associated with complications and hospital readmissions becomes a more significant consideration. Prior developed assessment metrics in the literature either consisted of hard-to-implement tools or relied on postoperative data to guide decision-making. This study aimed to develop a preoperative risk assessment tool to help predict the risk of hospital readmission and other postoperative adverse outcomes. METHODS: We retrospectively evaluated the 2019-2022(Q2) Medicare fee-for-service inpatient and outpatient claims data to identify primary anatomic or reserve TSAs and to predict postoperative adverse outcomes within 90 days post-discharge, including all-cause hospital readmissions, postoperative complications, emergency room visits, and mortality. We screened 108 candidate predictors, including demographics, social determinants of health, TSA indications, prior 12-month hospital and skilled nursing home admissions, comorbidities measured by hierarchical conditional categories, and prior orthopedic device-related complications. We used two approaches to reduce the number of predictors based on 80% of the data: 1) the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and 2) the machine-learning-based cross-validation approach, with the resulting predictor sets being assessed in the remaining 20% of the data. A scoring system was created based on the final regression models' coefficients, and score cutoff points were determined for low, medium, and high-risk patients. RESULTS: A total of 208,634 TSA cases were included. There was a 6.8% hospital readmission rate with 11.2% of cases having at least one postoperative adverse outcome. Fifteen covariates were identified for predicting hospital readmission with the area under the curve (AUC) of 0.70, and 16 were selected to predict any adverse postoperative outcome (AUC=0.75). The LASSO and machine learning approaches had similar performance. Advanced age and a history of fracture due to orthopedic devices are among the top predictors of hospital readmissions and other adverse outcomes. The score range for hospital readmission and an adverse postoperative outcome was 0 to 48 and 0 to 79, respectively. The cutoff points for the low, medium, and high-risk categories are 0-9, 10-14, ≥15 for hospital readmissions, and 0-11, 12-16, ≥17 for the composite outcome. CONCLUSION: Based on Medicare fee-for-service claims data, this study presents a preoperative risk stratification tool to assess hospital readmission or adverse surgical outcomes following TSA. Further investigation is warranted to validate these tools in a variety of diverse demographic settings and improve their predictive performance.

Probability of successful inhaled corticosteroids cessation in preschool wheezers: a predictive score.

Nearly all asthma predictive tools estimate the future risk of asthma development. However, there is no tool to predict the probability of successful ICS cessation at an early age. Therefore, we aimed to determine the predictors of successful ICS cessation in preschool wheezers, and developed a simple predictive tool for clinical practice. This was a retrospective cohort study involving preschool wheezers who had undergone an ICS therapeutic trial during 2015-2020 at the University Hospital, Southern, Thailand. A predictive scoring system was developed using a nomogram to estimate the probability of successful ICS cessation. We calculated area under ROC curve and used a calibration plot for assessing the tool's performance. A total of 131 medical records were eligible for analysis. Most of the participants were male (68.9%). More than half of the preschool wheezers had successful ICS cessation after an initial therapeutic trial regimen. The predictors of less successful ICS cessation were perinatal oxygen use [OR 0.10 (0.01, 0.70), P = 0.02], allergic rhinitis [OR 0.20 (0.08, 0.56), P = 0.002], blood eosinophil count > 500 cell/mm3 [OR 0.20 (0.06, 0.67), P = 0.008], and previous ICS use > 6 months [OR 0.30 (0.09, 0.72), P = 0.009]. CONCLUSIONS: Predictors of less successful ICS cessation were the following: perinatal oxygen use, allergic rhinitis, blood eosinophil count > 500 cell/mm3, and previous ICS use > 6 months. A simple predictive score developed in this study may help general practitioners to be more confident in making a decision regarding the discontinuation of ICS after initial therapeutic trials. WHAT IS KNOWN: • Early allergic sensitization is associated with reduced chances of inhaled corticosteroid cessation at school age. • Prolonged ICS is associated with the emergence of adverse effect and discontinuing too early can result in recurrence symptoms. WHAT IS NEW: • Requirement of oxygen support within 7 days after birth in term neonate is a postnatal factor associated with less successful ICS cessation. • We propose a simple predictive tool with easily available clinical parameters (perinatal oxygen use, allergic rhinitis, blood eosinophil count, parental asthma history, and duration of previous ICS use) to determine the timing of inhalational corticosteroid cessation in preschool wheezers.

Development of oral oncolytic nonadherence estimator (ORACLE): A pretreatment nonadherence risk assessment for oral oncolytics.

INTRODUCTION: To date, there is no adherence estimator to identify risk of nonadherence prior to initiating oral oncolytics. METHODS: A workgroup was assembled through the National Community Oncology Dispensing Association and tasked with creating a tool to meet this need. Tool constructs were defined after a review of the literature identifying top barriers to adherence. A second literature search was conducted to identify questions targeting specific barriers from validated adherence questionnaires. Once a finalized draft was complete, the risk assessment tool was built into an electronic survey where a risk category can be automatically calculated for the patient. RESULTS: The six most impactful factors affecting compliance to oral oncolytics were identified as patient's confidence, health literacy, perception of treatment, quality of life, social support, and complexity of chemotherapy regimen. A six-item questionnaire was created with five patient-directed questions and one clinician-directed question. Examples and descriptions were provided for clinicians to consider when categorizing complexity of a regimen. The tool was designed for responses to each question to be indexed into categories through a 10-point system. Results will be stratified into low, moderate, or high risk for nonadherence. CONCLUSION: The creation of a tool to predict nonadherence prior to starting therapy is an unmet need for patients initiating oral oncolytics. The aim of this tool is to meet those needs and better guide clinicians to provide patients with strategies to better manage nonadherence. Next steps include tool validation and piloting in clinical practice.

Performance in Kahoot! activities as predictive of exam performance.

BACKGROUND: Game-based learning (GBL) is effective for increasing participation, creativity, and student motivation. However, the discriminative value of GBL for knowledge acquisition has not yet been proven. The aim of this study is to assess the value of Kahoot! as a discriminative tool for formative assessment in medical education in two different subjects. METHODS: A prospective experimental study was conducted on a sample of 173 students enrolled in neuroanatomy (2021-2022). One hundred twenty-five students individually completed the Kahoot! prior to the final exam. In addition, students enrolled in human histology during two academic courses were included in the study. The control group course (2018-2019) received a traditional teaching methodology (N = 211), while Kahoot! was implemented during 2020-2021 (N = 200). All students completed similar final exams for neuroanatomy and human histology based on theory tests and image exams. RESULTS: The correlation between the Kahoot score and the final grade was analyzed for all students enrolled in neuroanatomy who completed both exercises. The correlation between the Kahoot exercise and the theory test, image exam and final grade was significantly positive in all cases (r = 0.334 p < 0.001, r = 0.278 p = 0.002 and r = 0.355 p < 0.001, respectively). Moreover, students who completed the Kahoot! exercise obtained significantly higher grades in all parts of the exam. Regarding human histology, the theory tests, image exams and final grades were significantly higher when using Kahoot! versus the "traditional" methodology (p < 0.001, p < 0.001 and p = 0.014, respectively). CONCLUSIONS: Our study demonstrates for the first time that Kahoot! can be used to improve and predict the final grade in medical education subjects.

Low initial in-hospital end-tidal carbon dioxide predicts poor patient outcomes and is a useful trauma bay adjunct.

INTRODUCTION: Appropriate triage of the trauma patient is critical. Low end-tidal carbon dioxide (ETCO2) is associated with mortality and hemorrhagic shock in trauma, but the relationship between low ETCO2 and important clinical variables is not known. This study investigates the association of initial in-hospital ETCO2 and patient outcomes, as well as the utility of ETCO2 as a predictive aid for blood transfusion. METHODS: Adult patients who presented to a Level One trauma center from 2019 to 2020 were eligible. Trauma bay ETCO2 measured by side-stream capnography was prospectively obtained for all trauma activations at time of initial evaluation. Using the Liu method of cut point estimation, patients were stratified as having low (≤29.5 mmHg) or normal ETCO2 (>29.5 mmHg). Multivariable regression was used to estimate the association of low ETCO2 with patient outcomes. RESULTS: A total of 955 patients underwent initial in-hospital ETCO2 measurement. Median time from arrival to ETCO2 measurement was 4 min. Among admitted patients (N = 493), 48.9% had low ETCO2. Compared to patients with normal ETCO2, those with low ETCO2 were older (median age 53 vs 46, p = 0.01) and more likely to have the highest trauma activation (27.4% vs 19.8%, p = 0.048). There was no difference in head injury. After adjustment, patients with low ETCO2 had greater odds of blood transfusion (OR 4.65, 95%CI 2.0-10.7), mortality (OR 5.10, 95%CI 1.1-24.9), inferior disposition (OR 1.64, 95%CI 1.1-2.6), and complications (OR 3.35, 95%CI 1.5-7.4). ETCO2 was more predictive of early blood transfusion than Shock Index (area under ROC = 67.6% vs 58.2%). CONCLUSIONS: Low trauma bay ETCO2 remains significantly associated with inferior clinical outcomes after adjustment. In comparison to other triage tools, low ETCO2 values may be more predictive of the need for blood transfusion. Further studies are needed to evaluate the role of ETCO2 as a decision making tool for early trauma management.

Real-world Implementation of an eHealth System Based on Artificial Intelligence Designed to Predict and Reduce Emergency Department Visits by Older Adults: Pragmatic Trial.

BACKGROUND: Frail older people use emergency services extensively, and digital systems that monitor health remotely could be useful in reducing these visits by earlier detection of worsening health conditions. OBJECTIVE: We aimed to implement a system that produces alerts when the machine learning algorithm identifies a short-term risk for an emergency department (ED) visit and examine health interventions delivered after these alerts and users' experience. This study highlights the feasibility of the general system and its performance in reducing ED visits. It also evaluates the accuracy of alerts' prediction. METHODS: An uncontrolled multicenter trial was conducted in community-dwelling older adults receiving assistance from home aides (HAs). We implemented an eHealth system that produces an alert for a high risk of ED visits. After each home visit, the HAs completed a questionnaire on participants' functional status, using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an ED visit within 14 days. In case of risk, the eHealth system alerted a coordinating nurse who could then inform the family carer and the patient's nurses or general practitioner. The primary outcomes were the rate of ED visits and the number of deaths after alert-triggered health interventions (ATHIs) and users' experience with the eHealth system; the secondary outcome was the accuracy of the eHealth system in predicting ED visits. RESULTS: We included 206 patients (mean age 85, SD 8 years; 161/206, 78% women) who received aid from 109 HAs, and the mean follow-up period was 10 months. The HAs monitored 2656 visits, which resulted in 405 alerts. Two ED visits were recorded following 131 alerts with an ATHI (2/131, 1.5%), whereas 36 ED visits were recorded following 274 alerts that did not result in an ATHI (36/274, 13.4%), corresponding to an odds ratio of 0.10 (95% IC 0.02-0.43; P<.001). Five patients died during the study. All had alerts, 4 did not have an ATHI and were hospitalized, and 1 had an ATHI (P=.04). In terms of overall usability, the digital system was easy to use for 90% (98/109) of HAs, and response time was acceptable for 89% (98/109) of them. CONCLUSIONS: The eHealth system has been successfully implemented, was appreciated by users, and produced relevant alerts. ATHIs were associated with a lower rate of ED visits, suggesting that the eHealth system might be effective in lowering the number of ED visits in this population. TRIAL REGISTRATION: clinicaltrials.gov NCT05221697; https://clinicaltrials.gov/ct2/show/NCT05221697.

A pattern-discovery-based outcome predictive tool integrated with clinical data repository: design and a case study on contrast related acute kidney injury.

BACKGROUND: Clinical data repositories (CDR) including electronic health record (EHR) data have great potential for outcome prediction and risk modeling. We built a prediction tool integrated with CDR based on pattern discovery and demonstrated a case study on contrast related acute kidney injury (AKI). METHODS: Patients undergoing cardiac catheterization from January 2015 to April 2017 were included. AKI was identified based on Acute Kidney Injury Network definition. Predictive model including 16 variables covered in existing AKI models was built. A visual analytics tool based on pattern discovery was trained on 70% data up to August 2016 with three interactive knowledge incorporation modes to develop 3 models: (1) pure data-driven, (2) domain knowledge, and (3) clinician-interactive, which were tested and compared on 30% consecutive cases dated afterwards. RESULTS: Among 2560 patients in the final dataset, 189 (7.3%) had AKI. We measured 4 existing models, whose areas under curves (AUCs) of receiver operating characteristics curve for the test dataset were 0.70 (Mehran's), 0.72 (Chen's), 0.67 (Gao's) and 0.62 (AGEF), respectively. A pure data-driven machine learning method achieves AUC of 0.72 (Easy Ensemble). The AUCs of our 3 models are 0.77, 0.80, 0.82, respectively, with the last being top where physician knowledge is incorporated. CONCLUSIONS: We developed a novel pattern-discovery-based outcome prediction tool integrated with CDR and purely using EHR data. On the case of predicting contrast related AKI, the tool showed user-friendliness by physicians, and demonstrated a competitive performance in comparison with the state-of-the-art models.

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments.

PURPOSE OF REVIEW: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. RECENT FINDINGS: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. SUMMARY: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.

Clinical prediction tool for extended-spectrum beta-lactamase-producing enterobacterales as the etiology of a bloodstream infection in solid organ transplant recipients.

BACKGROUND: Multidrug-resistant Gram-negative bacterial infections are increasingly common among solid organ transplant (SOT) recipients, leading to challenges in the selection of empiric antimicrobial therapy. We sought to develop a clinical tool to predict which SOT recipients are at high risk for extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales (EB) bloodstream infection (BSI). METHODS: A multicenter case-control study was performed. The source population included SOT recipients with an EB BSI between 2005 and 2018. Cases were those with ESBL-EB BSI; controls were those with non-ESBL EB BSI. The population was subdivided into derivation and validation cohorts based on study site. The predictive tool was developed in the derivation cohort through iterative multivariable logistic regression analyses that maximized the area under the receiver-operating curve (AUC). External validity was assessed using the validation cohort. RESULTS: A total of 897 SOT recipients with an EB BSI were included, of which 539 were assigned to the derivation cohort (135, 25% ESBL-EB) and 358 to the validation cohort (221, 62% ESBL-EB). Using multivariable analyses, the most parsimonious model that was predictive of ESBL-EB BSI consisted of 10 variables, which fell into four clinical categories: prior colonization or infection with EB organisms, recent antimicrobial exposures, severity of preceding illness, and immunosuppressive regimen. This model achieved an AUC of 0.81 in the derivation cohort and 0.68 in the validation cohort. CONCLUSIONS: Though further refinements are needed in additional populations, this tool shows promise for guiding empiric therapy for SOT recipients with EB BSI.

The Emergency Surgery Frailty Index (EmSFI): development and internal validation of a novel simple bedside risk score for elderly patients undergoing emergency surgery.

BACKGROUND: Frailty assessment has acquired an increasing importance in recent years and it has been demonstrated that this vulnerable profile predisposes elderly patients to a worse outcome after surgery. Therefore, it becomes paramount to perform an accurate stratification of surgical risk in elderly undergoing emergency surgery. STUDY DESIGN: 1024 patients older than 65 years who required urgent surgical procedures were prospectively recruited from 38 Italian centers participating to the multicentric FRAILESEL (Frailty and Emergency Surgery in the Elderly) study, between December 2016 and May 2017. A univariate analysis was carried out, with the purpose of developing a frailty index in emergency surgery called "EmSFI". Receiver operating characteristic curve analysis was then performed to test the accuracy of our predictive score. RESULTS: 784 elderly patients were consecutively enrolled, constituting the development set and results were validated considering further 240 consecutive patients undergoing colorectal surgical procedures. A logistic regression analysis was performed identifying different EmSFI risk classes. The model exhibited good accuracy as regard to mortality for both the development set (AUC = 0.731 [95% CI 0.654-0.772]; HL test χ2 = 6.780; p = 0.238) and the validation set (AUC = 0.762 [95% CI 0.682-0.842]; HL test χ2 = 7.238; p = 0.299). As concern morbidity, our model showed a moderate accuracy in the development group, whereas a poor discrimination ability was observed in the validation cohort. CONCLUSIONS: The validated EmSFI represents a reliable and time-sparing tool, despite its discriminative value decreased regarding complications. Thus, further studies are needed to investigate specifically surgical settings, validating the EmSFI prognostic role in assessing the procedure-related morbidity risk.

The LACE + index as a predictor of 90-day urologic surgery outcomes.

PURPOSE: This study assessed the ability of the LACE + [Length of stay, Acuity of admission, Charlson Comorbidity Index (CCI) score, and Emergency department visits in the past 6 months] index to predict adverse outcomes after urologic surgery. METHODS: LACE + scores were retrospectively calculated for all consecutive patients (n = 9824) who received urologic surgery at one multi-center health system over 2 years (2016-2018). Coarsened exact matching was employed to sort patient data before analysis; matching criteria included duration of surgery, BMI, and race among others. Outcomes including unplanned hospital readmission, emergency room visits, and reoperation were compared for patients with different LACE + quartiles. RESULTS: 722 patients were matched between Q1 and Q4; 1120 patients were matched between Q2 and Q4; 2550 patients were matched between Q3 and Q4. Higher LACE + score significantly predicted readmission within 90 days (90D) of discharge for Q1 vs Q4 and Q2 vs Q4. Increased LACE + score also significantly predicted 90D emergency room visits for Q1 vs Q4, Q2 vs Q4, and Q3 vs Q4. LACE + score was also significantly predictive of 90D reoperation for Q1 vs Q4. LACE + score did not predict 90D reoperation for Q2 vs Q4 or Q3 vs Q4 or 90D readmission for Q3 vs. Q4. CONCLUSION: These results suggest that LACE + may be a suitable prediction model for important patient outcomes after urologic surgery.

Predicting malignancy in patients with adrenal tumors using 18 F-FDG-PET/CT SUVmax.

BACKGROUND AND OBJECTIVES: 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG-PET/CT) parameters may help distinguish malignant from benign adrenal tumors, but few have been externally validated or determined based on definitive pathological confirmation. We determined and validated a threshold for 18 F-FDG-PET/CT maximum standard uptake value (SUVmax) in patients who underwent adrenalectomy for a nonfunctional tumor. METHODS: Database review identified patients with 18 F-FDG-PET/CT images available (training cohort), or only SUVmax values (validation cohort). Discriminative accuracy was assessed by area under the curve (AUC), and the optimal cutoff value estimated by maximally selected Wilcoxon rank statistics. RESULTS: Of identified patients (n = 171), 86 had adrenal metastases, 20 adrenal cortical carcinoma, and 27 adrenal cortical adenoma. In the training cohort (n = 96), SUVmax was significantly higher in malignant versus benign tumors (median 8.3 vs. 3.0, p < .001), with an AUC of 0.857. Tumor size did not differ. The optimal cutoff SUVmax was 4.6 (p < .01). In the validation cohort (n = 75), this cutoff had a sensitivity of 75% and specificity 55%. CONCLUSIONS: 18 F-FDG-PET/CT SUVmax was associated with malignancy. Validation indicated that SUVmax ≥ 4.6 was suggestive of malignancy, while lower values did not reliably predict benign tumor.

Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer.

PURPOSE: Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. METHODS: Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. RESULTS: The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. CONCLUSIONS: Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

Development and validation of a screening tool for early identification of bloodstream infection in older patients - a retrospective case-control study.

BACKGROUND: Delayed diagnosis of bloodstream infection (BSI) occurs in > 20% of older patients, with misdiagnosis in 35%. Our objective was to develop and validate a clinically useful screening tool to identify older patients with a high probability of having a BSI. METHODS: Hospitalized patients > 80 years old with BSI (n = 105/group) were evaluated for the tool development in this retrospective matched case-controlled study (learn cohort). The tool was validated in different retrospectively matched case and control patients > 80 years old (n = 120/group) and 65 to 79 years old (n = 250/group) (test cohort). Binary logistic regression was used to develop a screening tool using laboratory and clinical parameters that were significantly associated with BSI (P < 0.05; adjusted odds ratio (OR) > 1); and Classification and Regression Tree (CART) analysis was used to identify parameter breakpoints. Performance metrics were used to evaluate and validate the tool. RESULTS: The significant parameters associated with BSI were maximum temperature (Tmax)(> 37.55C)(OR = 42.575), neutrophils (> 7.95)(OR = 1.923), a change in level of consciousness (LOC) (Yes = 1, No = 0)(OR = 1.571), blood urea nitrogen (BUN)(> 10.05)(OR = 1.359), glucose (> 7.35)(OR = 1.167), albumin (< 33.5)(OR = 1.038) and alanine aminotransferase (ALT) (> 19.5)(OR = 1.005). The optimal screening tool [Ln (odds of BSI) = - 150.299 + 3.751(Tmax) + 0.654(neutrophils) + 0.452(change in LOC) + 0.307(BUN) + 0.154(glucose) + 0.038(albumin) + 0.005(ALT)] had favorable performance metrics in the learn and test cohorts (sensitivity, specificity and accuracy of 95% in the learn cohort and 77, 89, and 81% in the total test cohort); and performed better than using only temperature and neutrophil count. CONCLUSIONS: The validated tool had high predictive value which may improve early identification and management of BSI in older patients.

[The problem of global development of antibiotic resistant nosocomial pathogens].

The problem of global expansion of multidrug-resistant nosocomial infections pathogens is under special attention at the moment. Antibiotic resistance increasing give us the limited treatment options. This problem is particularly acute for transplant clinics, because of patients need lifelong immunosuppressive therapy. From the one hand this ensures stable allograft functioning, but from the other increases the risk of severe infectious complications in the postoperative period. The purpose of this article is analysis carbapenem resistance dynamics of Klebsiella spp., Acinetobacter spp., Pseudomonas spp. and Staphylococcus spp. isolated from the blood of recipients of donor organs from 2009 to 2019 in the Shumakov National Medical Research Center of Transplantology and Artificial Organs. A significant annual decrease of carbapenem-sensitive strains of Klebsiella spp. and Acinetobacter spp. are shown. The study of a distinctive pathogen resistance profile specific to each institution can help one in selecting an adequate antimicrobial strategy and is an effective predictive tool for controlling the growth of multidrug-resistant microorganisms.

Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients with Stage IV colorectal cancer.

BACKGROUND: Surgical resection of patients with resectable Stage IV colorectal cancer (CRC) is regarded as first choice if possible. However, its influence on overall survival (OS) has not been thoroughly explored. In this study, we aimed to construct nomograms to help predict 1-, 3- and 5-year OS rate and colorectal cancer-specific survival (CCSS) rate. METHODS: A total of 2996 cases who underwent primary and metastatic resection were selected in the study from surveillance, epidemiology and end results (SEER) database. About 48 Stage IV CRC patients after resection from the Fudan University Shanghai Cancer Center (FUSCC) were assigned as an independent external validation group. Log-rank and multivariate Cox regression analysis were used. The competing-risks model was used to estimate the cumulative incidence of death. Nomograms were built for prediction of OS and CCSS after surgical resection in patients with Stage IV CRC. RESULTS: The 1-, 3- and 5-year probabilities of OS were 76.6%, 41.4% and 23.2%, respectively. The 1-, 3- and 5-year colorectal cumulative incidence of death were 23.0%, 54.9% and 71.3%, respectively. The calibration curves for probability of 1-, 3- and 5-year OS and CCSS showed optimal agreement between nomogram prediction and actual observation, and the Harrell's C-indexes for the nomograms to predict OS and CCSS were 0.662 and 0.650, respectively. For FUSCC validation set, the C-index for this model to predict OS was 0.657. CONCLUSION: Nomograms for prediction of OS and CCSS of patients with Stage IV CRC who underwent primary and metastatic resection were built. Performance of the model was excellent. These nomograms may be helpful for patients and physicians when making a decision.

The Role of Social Support and Psychological Distress in Predicting Discharge: A Pilot Study for Hip and Knee Arthroplasty Patients.

BACKGROUND: Bundled payment initiatives for joint replacement have prompted re-evaluation of the continuum of care with emphasis on anticipating disposition needs. The purpose of this study is to investigate the role of social support and psychological distress in patient optimization after lower joint replacement. METHODS: Two hundred thirty-one patients undergoing elective joint replacement completed the Risk Assessment and Predictive Tool (RAPT) (social support assessment) and modified STarT Back Tool (mSBT) (assessment of pain-related psychological distress). Outcomes of interest were length of stay (LOS) and discharge location (home vs facility). RESULTS: No significant differences in mSBT scores were observed across RAPT levels when comparing individuals by discharge location (P > .05). There was significant indirect effect (0.07; P < .001) between mSBT and LOS. Therefore, the mSBT does not predict discharge location as a standalone metric for this sample. Mediation analysis for LOS indicates that higher psychological distress was predictive of longer LOS. Higher psychological distress and lower social support are associated with longer LOS. Despite higher psychological distress scores, higher social support scores are associated with shorter LOS. CONCLUSION: Analysis of this cohort suggests that pre-operative assessments of social and psychological constructs may provide preparatory information for patient discharge status. The RAPT is important for predicting LOS and discharge location. The mSBT may be important for predicting LOS for individuals with low to moderate social support.

Using landscape scenarios to improve local nitrogen management and planning.

Scenario-building is a widely used tool to initiate discussions on future land uses. In scenarios possible futures can be explored and peoples' ideas as well as societal trends can be visualized by the use of maps, pictures and figures. With focus on agricultural nitrogen management, and point of departure in the farmers' decisions-regarding fertilizer inputs, crop rotations, land use, and drainage, landscape scenarios are formulated based on local ideas for future nitrogen management and general prospects for local development. The key research question addressed in this paper is how landscape scenarios can guide farmers to improve nitrogen management in smaller catchments dominated by farming. Participatory modelling was used to develop landscape scenarios, depicting the change of nitrogen emission as a result of changes in landscape management and agricultural practices. In the development of the scenarios we used an ArcMap based tool combining statistical data, experimental knowledge, nitrate leaching modelling and input from local stakeholders on biophysical as well as land use and farm management issues. The scenarios presented are the result of a collaborative planning experiment within the frames of the dNmark research alliance on nitrogen. Three different types of scenarios are presented and discussed and their effects in terms of N reduction are estimated. The three scenarios were called: River valley set-aside, constructed wetlands, and land zonation. All the modelled scenarios are estimated to have a positive effect i.e. a reduction of the level of N leached to the root zone. Based on the experience gathered in the project, the feasibility of using scenarios for future environmental planning in the agricultural landscapes is discussed. Further, this is related to the current discussion in Denmark on geographically targeted nitrogen regulation. It is concluded that the co-creative approach to formulation of scenarios can be an effective way of increasing the knowledge and ownership of possible future solutions, however the cost associated with this planning approach is likely to substantially higher that more traditional planning approaches. Consequently, the estimated transactions costs should be weighed against the expected benefits in terms of more successful implementation.

Prediction of cesarean delivery in the term nulliparous woman: results from the prospective, multicenter Genesis study.

BACKGROUND: In contemporary practice many nulliparous women require intervention during childbirth such as operative vaginal delivery or cesarean delivery (CD). Despite the knowledge that the increasing rate of CD is associated with increasing maternal age, obesity and larger infant birthweight, we lack a reliable method to predict the requirement for such potentially hazardous obstetric procedures during labor and delivery. This issue is important, as there are greater rates of morbidity and mortality associated with unplanned CD performed in labor compared with scheduled CDs. A prediction algorithm to identify women at risk of an unplanned CD could help reduced labor associated morbidity. OBJECTIVE: In this primary analysis of the Genesis study, our objective was to prospectively assess the use of prenatally determined, maternal and fetal, anthropomorphic, clinical, and ultrasound features to develop a predictive tool for unplanned CD in the term nulliparous woman, before the onset of labor. MATERIALS AND METHODS: The Genesis study recruited 2336 nulliparous women with a vertex presentation between 39+0 and 40+6 weeks' gestation in a prospective multicenter national study to examine predictors of CD. At recruitment, a detailed clinical evaluation and ultrasound assessment were performed. To reduce bias from knowledge of these data potentially influencing mode of delivery, women, midwives, and obstetricians were blinded to the ultrasound data. All hypothetical prenatal risk factors for unplanned CD were assessed as a composite. Multiple logistic regression analysis and mathematical modeling was used to develop a risk evaluation tool for CD in nulliparous women. Continuous predictors were standardized using z scores. RESULTS: From a total enrolled cohort of 2336 nulliparous participants, 491 (21%) had an unplanned CD. Five parameters were determined to be the best combined predictors of CD. These were advancing maternal age (odds ratio [OR], 1.21; 95% confidence interval [CI], 1.09 to 1.34), shorter maternal height (OR, 1.72; 95% CI, 1.52 to 1.93), increasing body mass index (OR, 1.29; 95% CI, 1.17 to 1.43), larger fetal abdominal circumference (OR, 1.23; 95% CI, 1.1 to 1.38), and larger fetal head circumference (OR, 1.27; 95% CI, 1.14 to 1.42). A nomogram was developed to provide an individualized risk assessment to predict CD in clinical practice, with excellent calibration and discriminative ability (Kolmogorov-Smirnov, D statistic, 0.29; 95% CI, 0.28 to 0.30) with a misclassification rate of 0.21 (95% CI, 0.19 to 0.25). CONCLUSION: Five parameters (maternal age, body mass index, height, fetal abdominal circumference, and fetal head circumference) can, in combination, be used to better determine the overall risk of CD in nulliparous women at term. A risk score can be used to inform women of their individualized probability of CD. This risk tool may be useful for reassuring most women regarding their likely success at achieving an uncomplicated vaginal delivery as well as selecting those patients with such a high risk for CD that they should avoid a trial of labor. Such a risk tool has the potential to greatly improve planning hospital service needs and minimizing patient risk.

Early wound infection identification using the WIRE tool in community health care settings: An audit report.

Wound infection is proving to be a challenge for health care professionals. The associated complications and cost of wound infection is immense and can lead to death in extreme cases. Current management of wound infection is largely subjective and relies on the knowledge of the health care professional to identify and initiate treatment. In response, we have developed an infection prediction and assessment tool. The Wound Infection Risk-Assessment and Evaluation tool (WIRE) and its management strategy is a tool with the aim to bring objectivity to infection prediction, assessment and management. A local audit carried out indicated a high infection prediction rate. More work is being done to improve its effectiveness.