RESUMO
BACKGROUND: Suicide is a major global health concern, particularly among young people. This study evaluates an online suicide risk calculator based on the Risk Assessment of Suicidality Scale (RASS), which is designed to enhance accessibility and early detection of suicide risk. METHODS: The study involved 444 participants who completed the RASS via an online calculator. Results were compared with data from the COMET-G study's Russian sample (n=7572). Descriptive statistics, correlation analysis, and two-way ANOVA were used to analyze the data. RESULTS: The mean age of participants was 22.71 years (SD=7.94). The mean total RASS standardized score was 837.7 (SD=297.8). There was a significant negative correlation between age and RASS scores (r=-0.463, p<0.0001). The online calculator sample showed significantly higher RASS scores compared to the COMET-G sample, with 71% of online users scoring above the 90th percentile of the COMET-G sample. CONCLUSION: Our study demonstrated the advantage of the on-line suicidality risk calculator based on the RASS scale as a sensitive tool in detecting suicidal behaviours and measuring the severity of suicidality risks, offering a capability for broad reach and immediate assessment during clinical conversation between doctor and patient. Moreover, the RASS on-line psychometric instrument, when being freely distributed among the general population over internet sources, enabled to attract vulnerable groups of respondents with significantly higher suicidality risks. Future research should focus on integrating such tools into comprehensive suicide prevention programs and developing appropriate follow-up monitoring strategies for high risk-cases.
Assuntos
Prevenção do Suicídio , Humanos , Masculino , Feminino , Adulto , Medição de Risco/métodos , Adulto Jovem , Adolescente , Suicídio/psicologia , Ideação Suicida , Psicometria/instrumentação , Psicometria/normas , Pessoa de Meia-Idade , InternetRESUMO
BACKGROUND: Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. METHODS: Physicians providing care in outpatient community-based clinical settings (N = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. RESULTS: Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. CONCLUSIONS: The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. HIGHLIGHTS: The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition that of the clinical system.
RESUMO
Background: Acute kidney injury (AKI) after spinal fusion is a significant morbidity that can lead to poor post-surgical outcomes. Identifying AKI risk factors and developing a risk model can raise surgeons' awareness and allow them to take actions to mitigate the risks. The objective of the current study is to develop machine learning (ML) models to assess patient risk factors predisposing to AKI after posterior spinal instrumented fusion. Methods: Data was collected from the IBM MarketScan Database (2009-2021) for patients >18 years old who underwent spinal fusion with posterior instrumentation (3-6 levels). AKI incidence (defined by the International Classification of Diseases codes) was recorded 90-day post-surgery. Risk factors for AKI were investigated and compared through several ML models including logistic regression, linear support vector machine (LSVM), random forest, extreme gradient boosting (XGBoost), and neural networks. Results: Among the 141,697 patients who underwent fusion with posterior instrumentation (3-6 levels), the overall rate of 90-day AKI was 2.96%. We discovered that the logistic regression model and LSVM demonstrated the best predictions with area under the curve (AUC) values of 0.75. The most important AKI prediction features included chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. Patients who did not have these five key risk factors had a 90-day AKI rate of 0.29%. Patients who had an increasing number of key risk factors subsequently had higher risks of postoperative AKI. Conclusions: The analysis of the data with different ML models identified 5 key variables that are most closely associated with AKI: chronic renal disease, hypertension, diabetes mellitus ± complications, older age (>50 years old), and congestive heart failure. These variables constitute a simple risk calculator with additive odds ratio ranging from 3.38 (1 risk factor) to 91.10 (5 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for AKI risk, and potentially guide post-operative monitoring and medical management.
RESUMO
BACKGROUND: Open groin vascular surgeries are important in managing peripheral arterial diseases. Given its inherent risks and the diverse patient profiles, there is a need for risk assessment tools. This study aimed to develop a 30-d point-scoring risk calculator for patients undergoing open groin vascular surgeries. METHODS: Patients underwent open groin vascular surgery, including aortobifemoral, axillofemoral, femorofemoral, iliofemoral, femoral-popliteal, and femoral-tibial bypass as well as thromboendarterectomy, were identified in American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2021. Patients were randomly sampled into experimental (2/3) and validation (1/3) groups. The George Washington (GW) groin score, a weighted point-scoring system, was developed for 30-d mortality from multivariable regression on preoperative risk variables by Sullivan's method. GW groin score was subjected to internal and external validation. Furthermore, the effectiveness of GW groin score was evaluated in 30-d major surgical complications. RESULTS: A total of 129,424 patients were analyzed, with 86,715 allocated to experimental group and 42,709 to validation group. GW groin score is derived as follows: aortobifemoral bypass (2 points), axillofemoral bypass (1 point), age (>75 y, 2 points; 65-75 y, 1 point), disseminated cancer (2 points), emergent presentation (1 point), American Society of Anesthesiology score 4 or 5 (1 point), dialysis (1 point), and preoperative sepsis (1 point).GW groin score exhibited robust discrimination (c-statistic = 0.794, 95% CI = 0.786-0.803) and calibration (Brier score = 0.029). The transition from individual preoperative variables (c-statistic = 0.809, 95% CI = 0.801-0.818) to the point-scoring system was successful and external validation of the score was confirmed (c-statistic = 0.789, 95% CI = 0.777-0.801, Brier score = 0.030). Furthermore, GW groin score can effectively discriminate major surgical complications. CONCLUSIONS: This study developed GW groin score, a concise and comprehensive 10-point risk calculator. This well-validated score demonstrates robust discriminative and predictive abilities for 30-d mortality and major surgical complications following open groin vascular surgeries. GW groin score can anticipate potential perioperative complications and guide treatment decisions.
RESUMO
INTRODUCTION: While frailty has gained attention for its utility in risk stratification, no studies have directly compared them to existing risk calculators. The objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other than home. METHODS: Patients undergoing anatomic lung resection for primary, nonsmall cell lung cancer were identified within the ACS National Quality Improvement Program (ACS NSQIP) database. Tools were compared for discrimination in the primary outcomes. RESULTS: 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (IQR 59-74) years. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% CI). This included perioperative mortality (AUC 0.74, 95% CI 0.71-0.78), compared to RAI-rev (AUC 0.66, 95% CI 0.62-0.69) and 5-mFI (AUC 0.61, 95% CI 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. CONCLUSION: ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.
RESUMO
BACKGROUND: Published literature suggests "one-size-fits-all" infection prevention and control (IPC) staffing recommendations do not sufficiently account for program complexity needs. This project's objective was to create and validate a calculator utilizing risk and complexity factors to generate individualized IPC staffing ratios. METHODS: An online survey-based calculator was created that incorporated factors intended to predict staffing needs and multiple investigative questions to allow for optimization of factors in the algorithm. Hospital characteristics, staffing ratios, staffing perception, and outcomes were analyzed to determine the optimal questions and benchmarks for future releases. RESULTS: The median infection preventionist full-time equivalent to bed ratio was 121.0 beds for 390 participating hospitals. The calculator deemed 79.2% of respondent staffing as below expected. Significant association existed between higher standard infection ratio ranges and staffing status for central line-associated bloodstream infection (P = .02), catheter-associated urinary tract infections (P = .001), Clostridioides difficile infections (P = .003), and colon surgical site infections (P = .0001). CONCLUSIONS: This novel approach allows facilities to staff their IPC program based on individual factors. Future versions of the calculator will be optimized based on the findings. Future research will clarify the impact of staffing on patient outcomes and staff retention.
RESUMO
BACKGROUND: Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM). METHODS: Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values. RESULTS: Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9-46.7) versus 120.5 months (IQR 97.2-134.0), respectively (p < 0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/. CONCLUSIONS: An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.
RESUMO
Sepsis remains the second cause of death among neonates after the pathological consequences of extreme prematurity. In this review we summarized knowledge about pathogens causing early-onset sepsis (EOS) and late-onset sepsis (LOS), the role of perinatal risk factors in determining the EOS risk, and the tools used to reduce unnecessary antibiotics. New molecular assays could improve the accuracy of standard blood cultures, providing the opportunity for a quick and sensitive tool. Different sepsis criteria and biomarkers are available to date, but further research is needed to guide the use of antibiotics according to these tools. Beyond the historical antibiotic regimens in EOS and LOS episodes, antibiotics should be based on the local flora and promptly modulated if specific pathogens are identified. The possibility of an antibiotic lock therapy for central venous catheters should be further investigated. In the near future, artificial intelligence could help us to personalize treatments and reduce the increasing trend of multidrug-resistant bacteria.
RESUMO
Delivering adequate nutrition to preterm and sick neonates is critical for growth. Infants in the neonatal intensive care unit (NICU) require additional calories to supplement feedings for higher metabolic demands. Traditionally, clinicians enter free-text diet orders for a milk technician to formulate recipes, and dietitians manually calculate nutrition components to monitor growth. This daily process is complex and labor intensive with potential for error. Our goal was to develop an electronic health record (EHR)-integrated solution for entering feeding orders with automated nutrition calculations and mixing instructions. The EHR-integrated automated diet program (ADP) was created and implemented at a 52-bed level III academic NICU. The configuration of the parenteral nutrition orderable item within the EHR was adapted to generate personalized milk mixing recipes. Caloric, macronutrient, and micronutrient constituents were automatically calculated and displayed. To enhance administration safety, handwritten milk bottle patient labels were substituted with electronically generated and scannable patient labels. The program was further enhanced by calculating fortifier powder displacement factors to improve mixing precision. Order entry was optimized to allow for more complex mixing recipes and include a preference list of frequently ordered feeds. The EHR-ADP's safeguarded features allowed for catching multiple near-missed feeding administration errors. The NICU preterm neonate cohort had an average of 6-day decrease (P = 0.01) in the length of stay after implementation while maintaining the same weight gain velocity. The EHR-ADP may improve safety and efficiency; further improvements and wider utilization are needed to demonstrate the growth benefits of personalized nutrition.
RESUMO
PURPOSE: An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [68Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies. METHODS: A total of 683 consecutive biopsy-naïve men who underwent both [68Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies. RESULTS: The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873-0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0-40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5-40% for csPCa in both the development and validation cohorts. CONCLUSION: The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.
RESUMO
BACKGROUND: This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques. METHODS: Data on esophageal cancer patients from 2010 to 2020 were extracted from the surveillance, epidemiology, and end results (SEER) database. Through univariate and multivariate logistic regression analyses, eight indicators related to the risk of lung metastasis were selected. These indicators were incorporated into six machine learning classifiers to develop corresponding predictive models. The performance of these models was evaluated and compared using metrics such as The area under curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: A total of 20,249 confirmed cases of esophageal cancer were included in this study. Among them, 14,174 cases (70%) were assigned to the training set while 6075 cases (30%) constituted the internal test set. Primary site location, tumor histology, tumor grade classification system T staging criteria N staging criteria brain metastasis bone metastasis liver metastasis emerged as independent risk factors for esophageal cancer with lung metastasis. Amongst the six constructed models, the GBM algorithm-based machine learning model demonstrated superior performance during internal dataset validation. AUC, accuracy, sensitivity, and specificity values achieved by this model stood at respectively at 0.803, 0.849, 0.604, and 0.867. CONCLUSION: We have developed an online calculator based on the GBM model ( https://lvgrkyxcgdvo7ugoyxyywe.streamlit.app/)to aid clinical decision-making and treatment planning.
Assuntos
Neoplasias Esofágicas , Neoplasias Pulmonares , Aprendizado de Máquina , Programa de SEER , Humanos , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/secundário , Neoplasias Pulmonares/epidemiologia , Neoplasias Esofágicas/patologia , Neoplasias Esofágicas/epidemiologia , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Fatores de RiscoRESUMO
Health mindsets refer to beliefs about the malleability (growth mindset) versus stability (fixed mindset) of physical health and have gained traction as a predictor of health beliefs and behaviors. Across two studies, we tested whether health mindsets were associated with avoiding personalized health risk information. In Study 2, we also tested whether conceptually-related constructs of internal and chance health locus of control, health self-efficacy, fatalism, and genetic determinism were associated with information avoidance. Health mindsets were manipulated in Study 1 (college students, n = 284; 79.58% female; Mage = 19.74) and measured in Study 2 (participants recruited through MTurk, n = 735; 42.04% female; Mage = 35.78). In both studies, participants viewed a prediabetes infographic and were informed they could learn their prediabetes risk by completing an online risk calculator. Behavioral obligation was also manipulated in both studies to test whether an additional behavioral requirement associated with learning one's risk would exacerbate any negative impact of health mindsets on avoidance rates. All participants then indicated their interest in learning their prediabetes risk (avoidance intentions) and decided whether to complete the online risk calculator (avoidance behavior). In Study 1, there was no impact of health mindsets, behavioral obligation, or their interaction on avoidance intentions or behavior. Study 2 similarly did not provide consistent evidence for an association of health mindsets, behavioral obligation, or their interaction with avoidance intentions or behavior. However, in Study 2, internal health locus of control was consistently associated with both intentions and behavior. Health information avoidance may be a barrier to prevention and early detection of disease. To encourage individuals to learn potentially important health information, public health interventions might seek to increase people's beliefs that their own actions play a role in their health outcomes. Interventions may also seek to increase people's knowledge about and skills regarding improving their health outcomes, which may influence health locus of control beliefs.
Assuntos
Comportamentos Relacionados com a Saúde , Humanos , Feminino , Masculino , Adulto , Adulto Jovem , Conhecimentos, Atitudes e Prática em Saúde , Autoeficácia , Estado Pré-Diabético/psicologia , Aprendizagem da Esquiva , Adolescente , Controle Interno-Externo , Atitude Frente a SaúdeRESUMO
BACKGROUND: Hypertension is the most common reason for postpartum hospital readmission. Better prediction of postpartum readmission will improve the health care of patients. These models will allow better use of resources and decrease health care costs. OBJECTIVE: This study aimed to evaluate clinical predictors of postpartum readmission for hypertension using a novel machine learning (ML) model that can effectively predict readmissions and balance treatment costs. We examined whether blood pressure and other measures during labor, not just postpartum measures, would be important predictors of readmission. METHODS: We conducted a retrospective cohort study from the PeriData website data set from a single midwestern academic center of all women who delivered from 2009 to 2018. This study consists of 2 data sets; 1 spanning the years 2009-2015 and the other spanning the years 2016-2018. A total of 47 clinical and demographic variables were collected including blood pressure measurements during labor and post partum, laboratory values, and medication administration. Hospital readmissions were verified by patient chart review. In total, 32,645 were considered in the study. For our analysis, we trained several cost-sensitive ML models to predict the primary outcome of hypertension-related postpartum readmission within 42 days post partum. Models were evaluated using cross-validation and on independent data sets (models trained on data from 2009 to 2015 were validated on the data from 2016 to 2018). To assess clinical viability, a cost analysis of the models was performed to see how their recommendations could affect treatment costs. RESULTS: Of the 32,645 patients included in the study, 170 were readmitted due to a hypertension-related diagnosis. A cost-sensitive random forest method was found to be the most effective with a balanced accuracy of 76.61% for predicting readmission. Using a feature importance and area under the curve analysis, the most important variables for predicting readmission were blood pressures in labor and 24-48 hours post partum increasing the area under the curve of the model from 0.69 (SD 0.06) to 0.81 (SD 0.06), (P=.05). Cost analysis showed that the resulting model could have reduced associated readmission costs by US $6000 against comparable models with similar F1-score and balanced accuracy. The most effective model was then implemented as a risk calculator that is publicly available. The code for this calculator and the model is also publicly available at a GitHub repository. CONCLUSIONS: Blood pressure measurements during labor through 48 hours post partum can be combined with other variables to predict women at risk for postpartum readmission. Using ML techniques in conjunction with these data have the potential to improve health outcomes and reduce associated costs. The use of the calculator can greatly assist clinicians in providing care to patients and improve medical decision-making.
RESUMO
Introduction Scoliosis is characterized by an abnormal curvature of the spine in the coronal plane. Idiopathic scoliosis is the most prevalent type, though specific causes are sometimes identifiable. Genetic factors significantly influence adolescent idiopathic scoliosis (AIS), which is diagnosed through clinical and radiographic evaluations, primarily using the Cobb angle to measure curvature severity. The classification of scoliosis severity ranges from mild scoliosis, where sometimes the absence of pain is encountered, to moderate and severe, which is usually associated with lancinating pain. Early onset and high progression rates in idiopathic scoliosis are indicative of poorer prognoses. Methods The study analyzed 197 radiographic images from a private clinic database between December 2023 and April 2024. Inclusion criteria focused on anteroposterior images of the thorax and abdomen, excluding unclear and non-spinal images. Manual Cobb angle measurements were performed using RadiAnt DICOM Viewer 2020.2, followed by automated measurements using the Cobb Angle Calculator software. Discrepancies led to further image processing with enhanced color contrast for improved visualization. Data were analyzed using GraphPad InStat to assess error margins between manual and automated measurements. Results Initial results indicated discrepancies between manual and automated Cobb angle measurements. Enhanced image processing improved accuracy, demonstrating the efficacy of both manual and automated techniques in evaluating spinal deformities. Statistical analysis revealed significant error margins, prompting a refined approach for minimizing measurement errors. Discussion The study highlights the importance of accurate Cobb angle measurement in diagnosing and classifying scoliosis. Manual measurements, while reliable, are time-consuming and prone to human error. Automated methods, particularly those enhanced by machine learning algorithms, offer promising accuracy and efficiency. The integration of image processing techniques further enhances the reliability of scoliosis evaluation. Conclusion Accurate assessment of scoliosis through Cobb angle measurement is crucial for effective diagnosis and treatment planning. The study demonstrates that combining manual techniques with advanced automated methods and image processing significantly improves measurement accuracy. Such an approach is intended to support better clinical outcomes. Future research should focus on refining these technologies for broader clinical applications.
RESUMO
INTRODUCTION: Perioperative risk stratification is an essential component of preoperative planning for cancer surgery. While frailty has gained attention for its utility in risk stratification, no studies have directly compared it to existing risk calculators. Therefore, the objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other-than-home. METHODS: Patients undergoing anatomic lung resection for primary, non-small cell lung cancer were identified within the American College of Surgeons National Quality Improvement Program (ACS NSQIP) database. The ACS-SRC, RAI-rev, and 5-mFI tools were used to predict adverse postoperative events. Tools were compared for discrimination in the primary outcomes. RESULTS: 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (interquartile range = 59-74) years. Cardiothoracic surgeons performed 89% and general surgeons performed 11.0% of the operations. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% confidence interval [CI]). This included perioperative mortality (AUC = 0.74, 95% CI = 0.71-0.78), compared to RAI-rev (AUC = 0.66, 95% CI = 0.62-0.69) and 5-mFI (AUC = 0.61, 95% CI = 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. CONCLUSION: ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.
RESUMO
BACKGROUND: The Achilles tendon is the body's strongest and largest tendon. It is commonly injured, particularly among athletes, accounting for a significant portion of serious tendon injuries. Several factors play a precipitating role in increasing the risk of these injuries. OBJECTIVE: Our objective is to derive and validate a risk calculator for the prediction of incidence of any complication following Achilles tendon repair. METHODS: We used de-identified data from the American College of Surgeons' National Surgical Quality Improvement Project (NSQIP) database from 2005 to 2021. It comprises 7010 individuals who had undergone Achilles tendon rupture repair. Demographic and risk factors information was collected. To develop the calculator, the sample was divided into a derivation cohort (40%) and a validation cohort (60%). Multivariate logistic regression was used for statistical analysis, and a risk calculator for incidence of any complication was derived from the derivation cohort and validated on the remaining 60% of the sample. Patients with missing data were excluded, and the significance level was set at p < 0.05. RESULTS: We analyzed the derivation cohort of 2245 individuals who underwent Achilles tendon repair surgery between 2005 and 2021, with a 5.5% overall complication. Multivariate logistic regression identified anesthesia type, ASA classification, certain co-morbidities (pre-operative dialysis and medication-requiring hypertension), and wound classification as significant predictors of complications. The developed risk calculator model had an area under the curve (AUC) of 0.685 in the derivation cohort and 0.655 in the validation cohort, surpassing the widely used and validated modified frailty index. A cut-off score threshold of 0.06 was established using Youden's index to dichotomize individuals into low and high risk for developing any postoperative complications. CONCLUSION: Our risk calculator includes factors that most significantly affect the incidence of any complication following Achilles tendon repair.
Assuntos
Tendão do Calcâneo , Complicações Pós-Operatórias , Traumatismos dos Tendões , Humanos , Tendão do Calcâneo/lesões , Tendão do Calcâneo/cirurgia , Incidência , Masculino , Feminino , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Pessoa de Meia-Idade , Traumatismos dos Tendões/cirurgia , Traumatismos dos Tendões/epidemiologia , Ruptura/cirurgia , Ruptura/epidemiologia , Adulto , Medição de Risco/métodos , Fatores de Risco , Idoso , Estudos de CoortesRESUMO
INTRODUCTION: The traditional approach to neonatal early-onset sepsis (NEOS) management, involving maternal risk factors and nonspecific neonatal symptoms, usually leads to unnecessary antibiotic use. This study addresses these concerns by evaluating the Kaiser sepsis calculator (KSC) in guiding antibiotic therapy for NEOS, especially in high-incidence facilities (over 4/1,000 live births), by comparing it against the 2010 Centers for Disease Control and Prevention (CDC) guidelines for neonates ≥34 weeks with suspected sepsis, thereby emphasizing its implications for personalized patient care. METHODS: This is a prospective observational study. All neonates of 34 gestational weeks or more, presenting with either maternal risk factors or sepsis symptoms within 12 hours of birth, were included in the study. The analysis focused on antibiotic recommendations by the 2010 CDC guidelines versus those by the KSC at presumed (0.5/1,000) and actual (16/1,000) sepsis incidence rates. RESULTS: NEOS was identified in 14 cases (14.1%). Compared to the KSC, at an incidence rate of 16 per 1,000, the KSC resulted in a significant 32.3% reduction in antibiotic treatment (74 cases (74.7%) vs. 42 cases (42.4%), respectively; p < 0.001). The calculator advised immediate antibiotic utilization for 13 out of 14 (92.9%) diagnosed cases, suggesting further evaluation for the remaining cases. When a presumed incidence of 0.5/1,000 was applied, the KSC indicated antibiotics less frequently than when using the actual rate of 16/1,000 (p<0.001) with two missed NEOS cases. CONCLUSIONS: Using the KSC led to a decrease of 32 cases (32.3%) in unnecessary antibiotic prescriptions compared to adherence to 2010 CDC guidelines. However, setting a presumed incidence below the actual rate risked missing NEOS. The calculator was effective when actual local incidence rates were used, ensuring no missed cases needing antibiotics.
RESUMO
INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.
Assuntos
Neoplasias Colorretais , Hepatectomia , Neoplasias Hepáticas , Recidiva Local de Neoplasia , Humanos , Neoplasias Hepáticas/secundário , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/cirurgia , Masculino , Feminino , Neoplasias Colorretais/patologia , Pessoa de Meia-Idade , Idoso , Carga Tumoral , Metástase Linfática , Estudos Retrospectivos , Quimioterapia Adjuvante , Medição de Risco , Modelos de Riscos ProporcionaisAssuntos
Diabetes Mellitus Tipo 1 , Exercício Físico , Aplicativos Móveis , Humanos , Masculino , Feminino , Adulto , Pessoa de Meia-Idade , Adulto JovemRESUMO
Background and objective: The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa). Methods: Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis. Results: Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter. Conclusions and clinical implications: Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations. Patient summary: For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy.