Risk Calculator Research Articles

Case studies using a simple airborne infection risk calculator to minimize COVID-19 infection risk: Common approaches and challenges

Following the recognition of the role of airborne transmission in the spread of SARS-CoV-2, the role of building ventilation in minimising indoor respiratory events has become of significant interest, with numerous risk assessment tools developed to understand infection risk in different indoor environments. To date there has been limited retrospective analysis of how such tools were applied to assess indoor infection risk and inform building occupancy during the COVID-19 pandemic. In this paper we document case studies from Australia and New Zealand using one such risk assessment tool, the Airborne Infection Risk Calculator (AIRC), and describe how the AIRC was used to assess COVID-19 risk in different indoor settings and how users customized the tool for their own purposes. While inherent uncertainties mean the AIRC model could not calculate the exact risk in any of the case studies, the model's framework enabled objective discussion about the role of ventilation in infection risk reduction and public health. Future use of tools such as the AIRC would be improved with development of policies or regulations that promote or require a standardized approach for this assessment, translation of improvement in ventilation in a room or building scale into overall public health benefits, and endorsement of such tools by Global and/or National health authorities.

Metabolic syndrome risk prediction in an Australian sample with first-episode psychosis using the psychosis metabolic risk calculator: A validation study.

To examine the accuracy and likely clinical usefulness of the Psychosis Metabolic Risk Calculator (PsyMetRiC) in predicting up-to six-year risk of incident metabolic syndrome in an Australian sample of young people with first-episode psychosis. We conducted a retrospective study at a secondary care early psychosis treatment service among people aged 16-35years, extracting relevant data at the time of antipsychotic commencement and between one-to-six-years later. We assessed algorithm accuracy primarily via discrimination (C-statistic), calibration (calibration plots) and clinical usefulness (decision curve analysis). Model updating and recalibration generated a site-specific (Australian) PsyMetRiC version. We included 116 people with baseline and follow-up data: 73% male, mean age 20.1 years, mean follow-up 2.6years, metabolic syndrome prevalence 13%. C-statistics for both partial- (C = 0.71, 95% CI 0.64-0.75) and full-models (C = 0.72, 95% CI 0.65-0.77) were acceptable; however, calibration plots demonstrated consistent under-prediction of risk. Recalibration and updating led to slightly improved C-statistics, greatly improved agreement between observed and predicted risk, and a narrow window of likely clinical usefulness improved significantly. An updated and recalibrated PsyMetRiC model, PsyMetRiC-Australia, shows promise. Validation in a large sample is required to confirm its accuracy and clinical usefulness for the Australian population.

Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4.

We developed a scoring system for assessing glaucoma risk using demographic and laboratory factors by employing a no-code approach(automated coding) using ChatGPT-4. Comprehensive health checkup data were collected from the Korea National Health and Nutrition Examination Survey. Using ChatGPT-4, logistic regression was conducted to predict glaucoma without coding or manual numerical processes, and the scoring system was developed based on the odds ratios (ORs). ChatGPT-4 also facilitated the no-code creation of an easy-to-use risk calculator for glaucoma. The ORs for the high-risk groups were calculated to measure performance. ChatGPT-4 automatically developed a scoring system based on demographic and laboratory factors, and successfully implemented a risk calculator tool. The predictive ability of the scoring system was comparable to that of traditional machine learning approaches. For high-risk groups with 1-2, 3-4, and 5 + points, the calculated ORs for glaucoma were 1.87, 2.72, and 15.36 in the validation set, respectively, compared with the group with 0 or fewer points. This study presented a novel no-code approach for developing a glaucoma risk assessment tool using ChatGPT-4, highlighting its potential for democratizing advanced predictive analytics, making them readily available for clinical use in glaucoma detection.

Leveraging Electronic Health Records to Predict the Risk of Acute Kidney Injury after Allogeneic Hematopoietic Cell Transplantation.

The objective of this study is to assess the electronic health records (EHRs), which are potential risk factors for acute kidney injury (AKI) after allogenic hematopoietic cell transplantation (allo-HCT), and to propose a basic dataset and score for the calculation of HCT-acute kidney injury risk (HCT-AKIR). We undertook a retrospective analysis of the EHRs of 312 patients. Pre- and post-transplant factors were assessed, recognizing the following structured entries: laboratory data, encounters, medication, imaging studies, diagnoses, and procedures. Composite variables were used to create patient groups by combining two or more multivariate significant risk factors for AKI. The EHRs dataset and HCT-AKIR score were created based on the multivariate analysis of the composite variables. A multivariate analysis showed that previous CKD and once-impaired pre-transplant kidney function, sepsis, imaging procedures with contrast media, and cumulative length of intensive care unit stay after transplantation were significant risk factors. A new unit-weighted composite score based on the combination of significant risk factors contained in common EHR resources was proposed. Using our novel HCT-AKIR score calculated from the basic EHR dataset could be an easy way to increase awareness of post-transplant AKI and provide risk stratification.

Pharmaceuticals in biota: The impact of wastewater treatment plant effluents on fish in Australia

Globally, pharmaceuticals and personal care products (PPCPs) are detected in surface waters receiving wastewater, yet their presence in biota, remain largely understudied. To address this, we conducted a study that measured 46 PPCPs in spot water samples and fish caught up- and downstream from wastewater treatment plants (WWTPs) in Victoria, Australia. We sampled 15 sites located along four waterways following a 3-site design: WWTP-discharge(‘hotspot’), ‘upstream’(∼2 km) and ‘downstream’(∼2 km). Spot water and fish were also sampled at reference sites >100 km from WWTP discharge (n = 3). Additionally, spot water samples were taken from WWTP effluent outflows (n = 3). From each locality, we analysed 3–12 fish (n = 131 total). In waterways, passive samplers (POCIS; ∼28d, n = 19 PPCPs) were also deployed. Individual fish (axial muscle) and water were analysed with LC-MS-MS. We found that PPCP concentrations in environmental surface water ranged from<0.02–0.97 μg/L. In WWTP effluent, the range was <0.02–1.4 μg/L. Of the 46 PPCPs analysed, 12 were detected in spot water samples and five in fish. In water, the highest concentration detected was for antidepressant venlafaxine (3 μg/L). The most frequently detected PPCPs: venlafaxine (54.9%), metoprolol (41.2%), propranolol (29.4%), carbamazepine (29.4%), caffeine (17.6%) and sulfamethoxazole (17.6%). Out of 131 fish analysed, 35 fish had detectable levels of PPCPs in the muscle tissue. The highest muscle concentrations were: venlafaxine (150 μg/kg, redfin perch), and sertraline (100 μg/kg, eel). Bioaccumulation factors ranged from 104 to 341L/kg for venlafaxine in redfins, 21-1,260L/kg for carbamazepine in redfins and eels, and 367-3,333L/kg for sertraline in eels. Based on our human health risk calculations for venlafaxine, carbamazepine, sertraline, triclosan, and caffeine, consumption of fish does not pose a significant risk to human health. Despite this, most of the detected PPCPs in surface waters exceeded 10 ng/L trigger value, which has led to further investigations by EPA. Our study highlights the need for using multiple lines of evidence for estimating risks of PPCPs.

Optimizing Cardiovascular Health: A Comprehensive Review of Risk Assessment Strategies for Primary Prevention.

Cardiovascular disease (CVD) is a leading global health concern, and effective primary prevention strategies are essential to mitigate its impact. This comprehensive review examines current risk assessment strategies for primary prevention of CVD, emphasizing the importance of early identification and intervention to reduce disease incidence. Traditional risk factors such as hypertension, hyperlipidemia, smoking, and lifestyle choices are discussed alongside emerging factors, including genetic predispositions and biomarkers. The review evaluates various risk assessment tools and models, such as the Framingham risk score, atherosclerotic CVD risk calculator, QRISK, and Reynolds risk score, highlighting their methodologies, strengths, and limitations. Additionally, the review explores lifestyle modifications, including dietary changes, physical activity, weight management, smoking cessation, and pharmacological interventions like statins and antihypertensives. Special considerations for different populations, including the elderly, women, and those with a family history of CVD, are addressed. Future directions in cardiovascular risk assessment are also discussed, focusing on technological advancements and personalized medicine. This review aims to enhance the implementation of effective primary prevention measures and improve cardiovascular health outcomes by providing a thorough analysis of risk assessment strategies.

Assessment of survival prediction after surgery in spinal metastases patients using the Global Spine Study Tumor Group (GSTSG) risk calculator; an external validation from a tertiary cancer hospital.

We aim to validate the Global Spine Tumor Study Group (GSTSG) score compared to previous prognostic scoring systems in spinal metastasis. We conducted a retrospective study from January 2013 to December 2022. The survival prediction was compared between the GSTSG, Tomita Score, Revised Tokuhashi Score, and Skeletal Oncology Research Group (SORG) Nomogram. Single-variable factors associated with survival rate were analyzed using univariate Cox regression and multivariable Cox proportional hazard model. Receiver operating characteristic was used for external validity analysis at 3, 6, 12, and 24months. The overall survival rate was reported using the Kaplan-Meier survival curve. 248 spinal metastasis patients were included. The mean age was 59.23 ± 12.55years. The mean duration of follow-up time was 470.29 ± 441.98days. The external validity of GSTSG was the highest at all follow-up times (sufficiently accurate AUC > 0.7), which was about the same as SORG at 3months (both AUC of GSTSG and SORG = 0.76) and higher than modified Tokuhashi and Tomita score at 12months (AUC of GSTSG = 0.78, SORG = 0.71, Tomita = 0.64, and modified Tokuhashi = 0.61, respectively). From our study, the Multivariate Cox regression analysis indicates that the significant factors related to survival rate are regular analgesic use of weak opioids, lung metastasis, and previous chemotherapy. Compared to other traditional spinal metastases prognostic scoring systems, GSTSG shows the highest AUC for external validity in all follow-up times up to 24months.

Dynamics of indicators of the state of health and lifestyle of students in the process of studying at a social college

An anonymous survey was conducted on the author’s questionnaire of students of the social college of Moscow. The questionnaire included 26 questions related to health, complaints about well-being, fatigue, lifestyle, attitude to the chosen specialty. The study was cross-sectional. 289 people aged 15 to 19 were interviewed. Data processing was performed with the assessment of average values and the use of evidence-based medicine criteria and the calculation of health risks. The purpose of the research is to study the influence of health and lifestyle on the wellbeing of students and orientation to the chosen profession. The results showed that one in five had a chronic pathology with a predominance of diseases of the gastrointestinal tract and 20 % had reduced vision. 41.2 % of students were often ill in the first year, and by the third year their number was decreasing. Bad habits were common among students: 38.8 smoked in the first year, and half as much in the last. one in five people consumed alcoholic beverages. Good nutrition is important, but 13.8 %- 20 %., students rated their nutrition as poor. 20 % had a hot meal three times a day, and 30 % had it only once. The results indicate the need for classes with students on the basics of proper nutrition. Students with chronic diseases were more tired after classes, which teachers need to take into account. Among the students, the number of those who liked the profession increased in the dynamics of learning, which indicates the importance of practice.

Utility of dynamic contrast enhancement for clinically significant prostate cancer detection.

This study aimed to evaluate the association of dynamic contrast enhancement (DCE) with clinically significant prostate cancer (csPCa, Gleason Grade Group ≥2) and compare biparametric magnetic resonance imaging (bpMRI) and multiparametric MRI (mpMRI) nomograms. We identified a retrospective cohort of biopsy naïve patients who underwent pre-biopsy MRI separated by individual MRI series from 2018 to 2022. csPCa detection rates were calculated for patients with peripheral zone (PZ) lesions scored 3-5 on diffusion weighted imaging (DWI) with available DCE (annotated as - or +). bpMRI Prostate Imaging Reporting and Data System (PIRADS) (3 = 3-, 3+; 4 = 4-, 4+; 5 = 5-, 5+) and mpMRI PIRADS (3 = 3-; 4 = 3+, 4-, 4+; 5 = 5-, 5+) approaches were compared in multivariable logistic regression models. Nomograms for detection of csPCa and ≥GG3 PCa incorporating all biopsy naïve patients who underwent prostate MRI were generated based on available serum biomarkers [PHI, % free prostate-specific antigen (PSA), or total PSA] and validated with an independent cohort. Patients (n = 1010) with highest PIRADS lesion in PZ were included in initial analysis with 127 (12.6%) classified as PIRADS 3+ (PIRADS 3 on bpMRI but PIRADS 4 on mpMRI). On multivariable analysis, PIRADS 3+ lesions were associated with higher csPCa rates compared to PIRADS 3- (3+ vs. 3-: OR 1.86, p= 0.024), but lower csPCa rates compared to PIRADS DWI 4 lesions (4 vs. 3+: OR 2.39, p< 0.001). csPCa rates were 19% (3-), 31% (3+), 41.5% (4-), 65.9% (4+), 62.5% (5-), and 92.3% (5+). bpMRI nomograms were non-inferior to mpMRI nomograms in the development (n= 1410) and independent validation (n= 353) cohorts. Risk calculators available at: https://rossnm1.shinyapps.io/MynMRIskCalculator/. While DCE positivity by itself was associated with csPCa among patients with highest PIRADS lesions in the PZ, nomogram comparisons suggest that there is no significant difference in performance of bpMRI and mpMRI. bpMRI may be considered as an alternative to mpMRI for prostate cancer evaluation in many situations.

Assessing surgical risk calculators on hernia repair candidates with cirrhosis.

Several risk calculators have been developed and deployed to help surgeons estimate the mortality risk that comes with performing hernia repair surgery on patient with severe liver disease. This study seeks to evaluate the accuracy of these risk calculators on patients with cirrhosis and identify the most suitable population of patient to use these calculators on. The American College of Surgeons National Surgery Quality Improvement Program (NSQIP) 2013-2021 datasets were queried for patients who underwent hernia repair surgery. Mayo Clinic's "Post-operative Mortality Risk in Patients with Cirrhosis" risk calculator, Model for End-Stage Liver Disease (MELD) calculator, NSQIP's Surgical Risk Calculator, and a surgical 5-item modified frailty index were assessed to determine whether they accurately predict mortality following abdominal hernia repair. In total, 1368 patients met inclusion criteria. Receiver operating characteristic (ROC) curve analysis of the 4 mortality risk calculators resulted in the following: NSQIP Surgical Risk Calculator = 0.803 (p < 0.001); "Post-operative Mortality Risk in Patients with Cirrhosis" with an etiology of "Alcoholic or Cholestatic" yielded an AUC = 0.722 (p < 0.001); MELD score yielded an AUC = 0.709 (p < 0.001); and the modified 5-item frailty index yielded an AUC = 0.583 (p = 0.04). The NSQIP Surgical Risk Calculator more accurately predicts 30-day mortality in patients with ascites undergoing hernia repair. However, if the patient is missing one of the 21 input variables required by this calculator, Mayo Clinic's 30-day mortality calculator should be consulted before the more widely used MELD score.

Organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) distribution, origins, and risk evaluation in the Egyptian Mediterranean coast sediments.

A study was conducted on 31 surface sediments located in different sectors of the Egyptian Mediterranean coast. The sediments were analyzed for their pollution levels of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). The sediments were collected from various depths in harbors, coastal lakes, bays, and lagoons, covering the southeastern Mediterranean of the Nile Delta region. The study aimed at determining the distribution, origin, and potential ecological impact of OCP and PCB pollutants. The researchers used the SRM method of GC-MS/MS to measure the concentration of 18 PCBs and 16 OCPs residues. The study found that the total concentration of OCPs in the samples ranged from 3.091 to 20.512 ng/g, with a mean of 8.749 ±3.677 ng/g. The total concentration of PCB residues ranged from 2.926 to 20.77 ng/g, with a mean of 5.68 ±3.282 ng/g. The concentration of DDTs exceeded the effect range low (ERL) (1.00) and threshold effect level (TEL) (1.19) in several stations, but it was still below the effect range median (ERM) (7.00) and the probable effect level (PEL) (4.77). This indicates a low ecological risk. The principal component analysis (PCA) was also conducted to determine the sources of all pollutants in the sediment. The PCA showed significant correlations between the concentrations of Gama-HCH and Beta-HCH (0.741), suggesting similar sources. PRACTITIONER POINTS: OCPs and PCBs residues were analyzed in the sediment of the southeastern Mediterranean. The concentration, existence, and causes of OCPs and PCBs were investigated. OCPs and PCBs ecological risk and ecotoxicological calculation were investigated in detail. Cluster analysis, PCA, and correlation coefficient were also investigated.

Development and validation of a machine learning–based, point-of-care risk calculator for post-ERCP pancreatitis and prophylaxis selection

A robust model of post-ERCP pancreatitis (PEP) risk is not currently available. We aimed to develop a machine learning-based tool for PEP risk prediction to aid in clinical decision making related to periprocedural prophylaxis selection and postprocedural monitoring. Feature selection, model training, and validation were performed using patient-level data from 12 randomized controlled trials. A gradient-boosted machine (GBM) model was trained to estimate PEP risk, and the performance of the resulting model was evaluated using the area under the receiver operating curve (AUC) with 5-fold cross-validation. A web-based clinical decision-making tool was created, and a prospective pilot study was performed using data from ERCPs performed at the Johns Hopkins Hospital over a 1-month period. A total of 7389 patients were included in the GBM with an 8.6% rate of PEP. The model was trained on 20 PEP risk factors and 5 prophylactic interventions (rectal nonsteroidal anti-inflammatory drugs [NSAIDs], aggressive hydration, combined rectal NSAIDs and aggressive hydration, pancreatic duct stenting, and combined rectal NSAIDs and pancreatic duct stenting). The resulting GBM model had an AUC of 0.70 (65% specificity, 65% sensitivity, 95% negative predictive value, and 15% positive predictive value). A total of 135 patients were included in the prospective pilot study, resulting in an AUC of 0.74. This study demonstrates the feasibility and utility of a novel machine learning-based PEP risk estimation tool with high negative predictive value to aid in prophylaxis selection and identify patients at low risk who may not require extended postprocedure monitoring.

Potential United States Population Impact of the 2023 PREVENT Risk Calculator on Hypertension Management.

Potential United States Population Impact of the 2023 PREVENT Risk Calculator on Hypertension Management.

Critical appraisal of novel prediction models and risk calculators for post-hepatectomy liver failure and complications: practicability and generalisability in the real-world setting

Critical appraisal of novel prediction models and risk calculators for post-hepatectomy liver failure and complications: practicability and generalisability in the real-world setting

Changes in REVEAL Lite 2 risk status are associated with long-term outcomes in patients with pulmonary arterial hypertension: A post-hoc analysis of the GRIPHON study

BackgroundMortality risk assessment informs clinical management of pulmonary arterial hypertension (PAH). The Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) Lite 2 is a simplified risk calculator discriminating 1-year mortality risk. MethodsThis post-hoc analysis of the phase 3 GRIPHON study assessed changes in REVEAL Lite 2 risk score with selexipag versus placebo, and whether changes were prognostic or predictive of time to first morbidity/mortality (M/M) event. ResultsREVEAL Lite 2 risk category discriminated M/M risk (landmark concordance indices: 0.68-0.76, selexipag; 0.65-0.70, placebo). Across baseline risk categories, hazard ratios supported a lower risk of M/M events with selexipag versus placebo: low, 0.573 (95% confidence interval [CI] 0.361-0.908; p = 0.0178); intermediate, 0.423 (95% CI 0.274-0.655; p = 0.0001); high, 0.711 (9% CI 0.520-0.972; p = 0.0326). Odds ratios for risk improvement were 2.0 (95% CI 1.50-2.65), 1.8 (95% CI 1.38-2.43), and 2.0 (95% CI 1.43-2.72) for selexipag versus placebo at 16, 26, and 52 weeks, respectively (all p < 0.001). REVEAL Lite 2 risk improvement at Week 16 explained 19.1% of the treatment effect in all patients and 47.0% in patients with REVEAL Lite 2 baseline risk score of ≥ 7. ConclusionsREVEAL Lite 2 can monitor PAH M/M risk and facilitate treatment optimization. Baseline REVEAL Lite 2 risk score was prognostic of M/M risk in patients with PAH and mediates treatment effect up to 47% for those at higher risk. Lower M/M risk with selexipag versus placebo occurred irrespective of baseline risk category. (ClinicalTrials.gov identifier: NCT01106014)

Four-Strata Risk Assessment in Patients with Pulmonary Arterial Hypertension Treated with Selexipag in Real-World Settings (EXPOSURE Study).

Risk assessment can aid management of pulmonary arterial hypertension (PAH) and clinical decision-making. This analysis describes characteristics, treatment patterns and outcomes of patients with PAH, categorised by risk status at time of treatment escalation with selexipag in clinical settings. Patients initiating selexipag in the ongoing multicentre, prospective EXPOSURE (EUPAS19085) study were grouped as low, intermediate-low, intermediate-high or high risk of 1-year mortality according to the ESC/ERS 4-strata method. As of November 2022, 77% (535/698) of patients initiating selexipag had data allowing for risk calculation; 14% (N = 76) were low, 31% (N = 168) intermediate-low, 34% (N = 182) intermediate-high and 20% (N = 109) high risk of 1-year mortality. Overall, patients were predominantly female (71%), with idiopathic/heritable PAH (56%) or PAH associated with connective tissue disease (CTD-PAH; 27%), median age of 60years and prevalent (2years) disease. From low to high risk, proportion of CTD-PAH and age increased (from 12%-40% and 46-68years, respectively); time from diagnosis decreased and presence of cardiovascular risk factors increased. Most patients across risk groups (74-81%) initiated selexipag as part of triple oral combination therapy. Overall median (Q1, Q3) selexipag exposure duration was 10.1 (3.5, 24.1) months. Proportions of hospitalised patients increased with increasing risk group (16-42% from low to high, respectively); more hospitalisations were PAH-related for the high risk (71%) versus other risk groups (47-54%). Kaplan-Meier survival estimates were 98%, 98%, 93% and 80% at 1-year and 98%, 92%, 81% and 67% at 2-years, from low to high risk, respectively. In clinical settings, selexipag is initiated across all risk groups, predominantly as triple therapy. Only 45% of patients being at low/intermediate-low risk at selexipag initiation suggests an opportunity for more frequent patient monitoring and earlier treatment escalation, given that 4-strata risk assessment was prognostic for hospitalisations and survival in this contemporary PAH cohort. A graphical abstract is available with this article.

Cascade failure risk analysis of mixed reservoirs based on disaster spreading theory

The reservoirs in the basin are connected in series or in parallel, forming a mixed reservoir group. Failure of a single dam in a mixed reservoir group may lead to subsequent failures of downstream dams, resulting in a cascading failure of the reservoir group. However, existing studies mainly focus on the impact of the upstream dam failure on the downstream dams, and lack a comprehensive consideration of the risk level of the upstream dams, the dam's own risk, and its own warning and flood release capacity. This limitation affects the accuracy of the actual risk calculation results of mixed reservoir dams. In this paper, the cascading failure risk chain is determined using graph theory, and a cascade failure risk analysis model based on disaster spreading theory is developed by combining dam break flood simulation and reservoir flood routing theory. The cascade failure risk is assessed in terms of the probability and the duration of a cascading failure. The model is applied to six mixed reservoirs in the Wujiang River Basin, China. The sensitivity of the model parameters is analyzed by examining a specific node, and the feasibility and effectiveness of the model are verified.

Predicting Surgical Outcomes Using Wearables

Accurate prediction of readmission and severe complication risks during the pre-operative stage is crucial for clinical decision-making. In addition to traditional static clinical data, the activity and physiological data collected by commercial wearable devices such as Fitbit wristbands provide valuable complementary information. However, data from wearables are often incomplete and noisy, presenting challenges in extracting clinically meaningful features from fine-grained time series data. In this paper, we propose a pipeline to develop predictive models using pre-operative wearable data to predict surgical outcomes in patients undergoing pancreatic surgeries. The core of this pipeline is a multi-level feature engineering framework designed to extract high-level features from wearable data, which are then combined with static clinical characteristics to form the input for the predictive model. In a clinical study involving 61 patients, our approach improved the Area Under the Receiver Operating Characteristic (AUROC) by 44% compared to the existing risk calculator employed in clinical practice.

Anticipated Morbidities Assessed by NSQIP Surgical Risk Calculator in Patients with Pneumoperitoneum Undergoing Laparotomy

Background: The American College of Surgery National Surgical Quality Improvement Program (NSQIP) calculator is a tool to assess the risk stratification of a patient undergoing major surgical procedures. We used this calculator to assess morbidities in patients with pneumoperitoneum. Method: A prospective study of patients with pneumoperitoneum undergoing exploratory laparotomy was conducted in the General Surgery Department, Liaquat National Hospital. Surgical risks were calculated preoperatively and were compared with the actual outcomes. Results: The NSQIP surgical risk calculator calculated morbidities in comparison with actual morbidities, which were surgical site infection in 28 (17.8%) vs. 49 (31.2%) patients, urinary tract infection in 26 (16.6%) vs. 4 (2.5%), pneumonia in 28 (17.8%) vs. 24 (15.3%) and cardiac event in 25 (15.9%) vs. 14 (8.9%). Conclusion: The NSQIP surgical risk calculator adequately anticipated morbidities such as SSI, UTI, pneumonia, and cardiac events in comparison with actual morbidities.

The Association Between the Australian Cardiovascular Risk Calculator With Long-Term Atherosclerotic Vascular Disease Hospitalisations and Deaths in Community-Dwelling Older Women

The Association Between the Australian Cardiovascular Risk Calculator With Long-Term Atherosclerotic Vascular Disease Hospitalisations and Deaths in Community-Dwelling Older Women