Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer

BACKGROUND

The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups.

OBJECTIVE

To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms.

DESIGN, SETTING, AND PARTICIPANTS

Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined.

RESULTS AND LIMITATIONS

There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups.

CONCLUSIONS

The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer.

PATIENT SUMMARY

We previously developed a calculator that predicts patients' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.

Hematuria Cancer Risk Score with Ultrasound Informs Cystoscopy Use in Patients with Hematuria

BACKGROUND

Hematuria is a cardinal symptom of urinary tract cancer and would require further investigations.

OBJECTIVE

To determine the ability of renal bladder ultrasound (RBUS) with the Hematuria Cancer Risk Score (HCRS) to inform cystoscopy use in patients with hematuria.

DESIGN, SETTING, AND PARTICIPANTS

The development cohort comprised 1984 patients with hematuria from 40 UK hospitals (DETECT 1; ClinicalTrials.gov: NCT02676180) who received RBUS. An independent validation cohort comprised 500 consecutive patients referred to secondary care for a suspicion of bladder cancer.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Sensitivity and true negative of the HCRS and RBUS were assessed.

RESULTS AND LIMITATIONS

A total of 134 (7%) and 36 (8%) patients in the development and validation cohorts, respectively, had a diagnosis of urinary tract cancer. Validation of the HCRS achieves good discrimination with an area under the receiver operating characteristic curve of 0.727 (95% confidence interval 0.648-0.800) in the validation cohort with sensitivity of 95% for the identification of cancer. Utilizing the cutoff of 4.500 derived from the HCRS in combination with RBUS in the development cohort, 680 (34%) patients would have been spared cystoscopy at the cost of missing a G1 Ta bladder cancer and a urinary tract cancer patient, while 117 (25%) patients would have avoided cystoscopy at the cost of missing a single patient of G1 Ta bladder cancer with sensitivity for the identification of cancer of 97% in the validation cohort.

CONCLUSIONS

The HCRS with RBUS offers good discriminatory ability in identifying patients who would benefit from cystoscopy, sparing selected patient cohorts from an invasive procedure.

PATIENT SUMMARY

The hematuria cancer risk score with renal bladder ultrasound allows for the triage of patients with hematuria who would benefit from visual examination of the bladder (cystoscopy). This resulted in 25% of patients safely omitting cystoscopy, which is an invasive procedure, and would lead to health care cost savings.

Risk prediction of bladder cancer among person with diabetes: A derivation and validation study

AIMS

This study aimed to devise and validate a clinical scoring system for risk prediction of bladder cancer to guide urgent cystoscopy evaluation among people with diabetes.

METHODS

People with diabetes who received cystoscopy from a large database in the Chinese population (2009-2018). We recruited a derivation cohort based on random sampling from 70% of all individuals. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 5: 0-2 'average risk' (AR) and 3-5 'high risk' (HR).

RESULTS

A total of 5905 people with diabetes, among whom 123 people with BCa were included. The prevalence rate in the derivation (n  = 4174) and validation cohorts (n  = 1731) was 2.2% and 1.8% respectively. Using the scoring system constructed, 79.6% and 20.4% in the derivation cohort were classified as AR and HR respectively. The prevalence rate in the AR and HR groups was 1.57% and 4.58% respectively. The risk score consisted of age (18-70: 0; >70: 2), male sex (1), ever/ex-smoker (1) and duration of diabetes (≥10 years: 1). Individuals in the HR group had 3.26-fold (95% CI = 1.65-6.44, p  = 0.025) increased prevalence of bladder than the AR group. The concordance (c-) statistics was 0.72, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider earlier cystoscopy.

CONCLUSIONS

The risk prediction algorithm may inform urgency of cystoscopy appointments, thus allowing a more efficient use of resources and contributing to early detection of BCa among people planned to be referred.

Development and Validation of a Dynamic Nomogram for Predicting 3-Month Mortality in Acute Ischemic Stroke Patients with Atrial Fibrillation

Background

Acute ischemic stroke (AIS) in patients with atrial fibrillation (AF) carries a substantial risk of mortality, emphasizing the need for effective risk assessment and timely interventions. This study aimed to develop and validate a practical dynamic nomogram for predicting 3-month mortality in AIS patients with AF.

Methods

AIS patients with AF were enrolled and randomly divided into training and validation cohorts. The nomogram was developed based on independent risk factors identified by multivariate logistic regression analysis. The prediction performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, decision curve analysis (DCA), and Kaplan-Meier survival analysis.

Results

A total of 412 patients with AIS and AF entered final analysis, 288 patients in the training cohort and 124 patients in the validation cohort. The nomogram was developed using age, baseline National Institutes of Health Stroke Scale score, early introduction of novel oral anticoagulants, and pneumonia as independent risk factors. The nomogram exhibited good discrimination both in the training cohort (AUC, 0.851; 95% CI, 0.802-0.899) and the validation cohort (AUC, 0.811; 95% CI, 0.706-0.916). The calibration plots, DCA and Kaplan-Meier survival analysis demonstrated that the nomogram was well calibrated and clinically useful, effectively distinguishing the 3-month survival status of patients with AIS and AF, respectively. The dynamic nomogram can be obtained at the website: https://yanxiaodi.shinyapps.io/3-monthmortality/.

Conclusion

The dynamic nomogram represents the first predictive model for 3-month mortality and may contribute to managing the mortality risk of patients with AIS and AF.

Development and Validation of a Prediction Model for 30-Day Mortality and Functional Outcome in Patients with Primary Brainstem Hemorrhage

INTRODUCTION

Primary brainstem hemorrhage (PBSH) is the most fatal subtype of intracerebral hemorrhage and is associated with poor prognosis. We aimed to develop a prediction model for predicting 30-day mortality and functional outcome in patients with PBSH.

METHODS

We reviewed records of 642 consecutive patients with first-time PBSH from three hospitals between 2016 and 2021. Multivariate logistic regression was used to establish a nomogram in a training cohort. Cutoff points of the variables were determined by receiver operating characteristic curve analysis, and certain points were assigned to these predictors to produce the PBSH score. The nomogram and PBSH score were compared with other scoring systems for PBSH.

RESULTS

Five independent predictors, comprised of temperature, pupillary light reflex, platelet-to-lymphocyte ratio, Glasgow Coma Scale (GCS) score on admission, and hematoma volume, were incorporated to construct the nomogram. The PBSH score consisted of 4 independent factors with individual points assigned as follows: temperature, ≥38°C (=1 point), <38°C (=0 points); pupillary light reflex, absence (=1 point), presence (=0 points); GCS score 3-4 (=2 points), 5-11 (=1 point), and 12-15 (=0 points); PBSH volume >10 mL (=2 points), 5-10 mL (=1 point), and <5 mL (=0 points). Results showed that the nomogram was discriminative in predicting both 30-day mortality (area under the ROC curve [AUC] of 0.924 in the training cohort, and 0.931 in the validation cohort) and 30-day functional outcome (AUC of 0.887). The PBSH score was discriminative in predicting both 30-day mortality (AUC of 0.923 in the training cohort and 0.923 in the validation cohort) and 30-day functional outcome (AUC of 0.887). The prediction performances of the nomogram and the PBSH score were superior to the intracranial hemorrhage (ICH) score, primary pontine hemorrhage (PPH) score, and new PPH score.

CONCLUSIONS

We developed and validated two prediction models for 30-day mortality and functional outcome in patients with PBSH. The nomogram and PBSH score could predict 30-day mortality and functional outcome in PBSH patients.

Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study

BACKGROUND

Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation.

OBJECTIVE

To develop a prediction model for urinary tract cancer in patients referred with haematuria.

DESIGN, SETTING, AND PARTICIPANTS

A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism.

RESULTS AND LIMITATIONS

The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy.

CONCLUSIONS

This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation.

PATIENT SUMMARY

We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation.

Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review

BACKGROUND

Timely diagnosis of bladder and kidney cancer is key to improving clinical outcomes. Given the challenges of early diagnosis, models incorporating clinical symptoms and signs may be helpful to primary care clinicians when triaging at-risk patients.

AIM

To identify and compare published models that use clinical signs and symptoms to predict the risk of undiagnosed prevalent bladder or kidney cancer.

DESIGN AND SETTING

Systematic review.

METHOD

A search identified primary research reporting or validating models predicting the risk of bladder or kidney cancer in MEDLINE and EMBASE. After screening identified studies for inclusion, data were extracted onto a standardised form. The risk models were classified using TRIPOD guidelines and evaluated using the PROBAST assessment tool.

RESULTS

The search identified 20 661 articles. Twenty studies (29 models) were identified through screening. All the models included haematuria (visible, non-visible, or unspecified), and seven included additional signs and symptoms (such as abdominal pain). The models combined clinical features with other factors (including demographic factors and urinary biomarkers) to predict the risk of undiagnosed prevalent cancer. Several models (n = 13) with good discrimination (area under the receiver operating curve >0.8) were identified; however, only eight had been externally validated. All of the studies had either high or unclear risk of bias.

CONCLUSION

Models were identified that could be used in primary care to guide referrals, with potential to identify lower-risk patients with visible haematuria and to stratify individuals who present with non-visible haematuria. However, before application in general practice, external validations in appropriate populations are required.

Assessment of Diagnostic Yield of Cystoscopy and Computed Tomographic Urography for Urinary Tract Cancers in Patients Evaluated for Microhematuria: A Systematic Review and Meta-analysis

IMPORTANCE

Microhematuria (MH) is a common finding that often leads to further evaluation for urinary tract cancers. There is ongoing debate about the extent to which patients with MH should be evaluated for cancer.

OBJECTIVE

To assess the diagnostic yield for detection of urinary tract cancers, specifically bladder cancer, upper tract urothelial carcinoma (UTUC), and kidney cell carcinoma, among patients evaluated for MH using cystoscopy and computed tomographic (CT) urography.

DATA SOURCES

MEDLINE, Scopus, and Embase were systematically searched for eligible studies published between January 1, 2009, and December 31, 2019.

STUDY SELECTION

Original prospective and retrospective studies reporting the prevalence of cancer among patients evaluated for MH were eligible. Two authors independently screened the titles and abstracts to select studies that met the eligibility criteria and reached consensus about which studies to include. Among 5802 records identified, 5802 articles were screened using titles and abstracts. After exclusions, 55 full-text articles were assessed for eligibility, with 39 studies selected for systematic review.

DATA EXTRACTION AND SYNTHESIS

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Studies were quantitatively synthesized using a random-intercept logistic regression model.

MAIN OUTCOMES AND MEASURES

The primary outcome was diagnostic yield, defined as the proportion of patients with a diagnosis of urinary tract cancer (bladder cancer, UTUC, or kidney cell carcinoma) after presentation with MH. Studies were stratified by the percentage of cystoscopy and CT urography use and by high-risk cohorts. The diagnostic yields of CT urography and cystoscopy were calculated for each cancer type.

RESULTS

A total of 30 studies comprising 24 366 patients evaluated for MH were included in the meta-analysis. The pooled diagnostic yield among all patients was 2.00% (95% CI, 1.30%-3.09%) for bladder cancer, 0.02% (95% CI, 0.0%-0.15%) for UTUC, and 0.18% (95% CI, 0.09%-0.36%) for kidney cell carcinoma. Stratification of studies that used cystoscopy and/or CT urography for 95% or more of the cohort produced diagnostic yields of 2.74% (95% CI, 1.81%-4.12%) for bladder cancer, 0.09% (95% CI, 0.01%-0.75%) for UTUC, and 0.10% (95% CI, 0.04%-0.23%) for kidney cell carcinoma. In high-risk cohorts, the diagnostic yields increased to 4.61% (95% CI, 2.34%-8.90%) for bladder cancer and 0.45% (95% CI, 0.22%-0.95%) for UTUC.

CONCLUSIONS AND RELEVANCE

This study's findings suggest that, given the low diagnostic yield of CT urography and the associated risks and costs, limiting its use to high-risk patients older than 50 years is warranted. Risk stratification, as recommended by the recent American Urology Association guidelines on MH, may be a better approach to tailor further evaluation.

Prolonged Diagnostic Intervals as Marker of Missed Diagnostic Opportunities in Bladder and Kidney Cancer Patients with Alarm Features: A Longitudinal Linked Data Study

BACKGROUND

In England, patients who meet National Institute for Health and Care Excellence (NICE) guideline criteria for suspected cancer should receive a specialist assessment within 14 days. We examined how quickly bladder and kidney cancer patients who met fast-track referral criteria were actually diagnosed.

METHODS

We used linked primary care and cancer registration data on bladder and kidney cancer patients who met fast-track referral criteria and examined the time from their first presentation with alarm features to diagnosis. Using logistic regression we examined factors most likely to be associated with non-timely diagnosis (defined as intervals exceeding 90 days), adjusting for age, sex and cancer type, positing that such occurrences represent missed opportunity for timely referral, possibly due to sub-optimal guideline adherence.

RESULTS

28%, 42% and 31% of all urological cancer patients reported no, one or two or more relevant symptoms respectively in the year before diagnosis. Of the 2105 patients with alarm features warranting fast-track assessment, 1373 (65%) presented with unexplained haematuria, 382 (18%) with recurrent urinary tract infections (UTIs), 303 (14%) with visible haematuria, and 45 (2%) with an abdominal mass. 27% overall, and 24%, 45%, 18% and 27% of each group respectively, had a non-timely diagnosis. Presentation with recurrent UTI was associated with longest median diagnostic interval (median 83 days, IQR 43-151) and visible haematuria with the shortest (median 50 days, IQR 30-79). After adjustment, presentation with recurrent UTIs, being in the youngest or oldest age group, female sex, and diagnosis of kidney and upper tract urothelial cancer, were associated with greater odds of non-timely diagnosis.

CONCLUSION

More than a quarter of patients presenting with fast-track referral features did not achieve a timely diagnosis, suggesting inadequate guideline adherence for some patients. The findings highlight a substantial number of opportunities for expediting the diagnosis of patients with bladder or kidney cancers.

Evidence-based diagnostic accuracy measurement in urine cytology using likelihood ratios

INTRODUCTION

Recent cytology classification systems have become more evidence-based and advocate for the use of risk of malignancy (ROM) as a measure of test performance. From the statistical viewpoint, ROM represents the post-test probability of malignancy, which changes with the test result and also with the prevalence of malignancies (or pre-test probability) in each individual practice setting and individual patient presentation. Evidence-based medicine offers likelihood ratios (LRs) as a measure of diagnostic accuracy for multilevel diagnostic tests, superior to sensitivity and specificity as data binarization and information loss are avoided. LRs are used in clinical medicine and could be successfully applied to the practice of cytopathology. Our aim was to establish LRs to compare diagnostic accuracy of The Paris System for Reporting Urinary Cytology (TPS) and of a historic urine cytology reporting system.

MATERIALS AND METHODS

We analyzed sequential voided urine cytology cases with histologic outcomes: 188 pre-TPS and 167 post-TPS. LRs were calculated as LR = True positive % (per category)/False positive % (per category) [95% confidence interval] and interpreted LRs = 1 nondiagnostic, LR >1 favor, LR >10 strongly favor, LRs <1 favor exclusion, and LR <0.1 strongly favor exclusion of a target condition, respectively. CATmaker open source software and Fagan nomograms were used for calculation and visualization of the corresponding post-test probability (ROM) of high-grade urothelial carcinoma (HGUC) in various scenarios.

RESULTS

Both reporting systems show near-similar performance in terms of LRs, with moderate discriminatory power of negative, suspicious, and positive for HGUC test results. The atypical urothelial cell (AUC) category establishes as indiscriminate LR = 1 in the TPS, whereas in pre-TPS it favored a benign condition. We further demonstrate the utility of LRs to determine individual post-test probability (ROM) in a variety of clinical scenarios in a personalized fashion.

CONCLUSIONS

The LRs allow for a quantitative performance measure in case of urine cytology across different scenarios adding numeric information on diagnostic test accuracy and post-test probability of HGUC. The diagnostic accuracy of pre-TPS and post-TPS remained similar for all but the AUC category. With the TPS, the AUC category has become genuinely diagnostically and statistically indeterminate and requires further patient investigations.

A simplified nomogram to assess risk of bladder cancer in patients with a new diagnosis of microscopic hematuria

INTRODUCTION

The vast majority of patients who undergo a diagnostic evaluation for microscopic hematuria (MH) do not have occult bladder cancer. Identifying patients with MH at high risk of harboring bladder cancer can allow for a risk adjusted approach to diagnostic interventions with the goal of safely reducing unnecessary evaluations.

METHODS

Patients with a new diagnosis of microhematuria during an 8.5 year period were retrospectively identified. All patients who had a complete MH evaluation were randomized to a training or a validation cohort. Logistic regression analysis was performed in the training cohort to identify factors related to a bladder cancer diagnosis and to develop our model. Receiver operating curves to identify bladder cancer were constructed for the training and validation cohort and tested for their ability to discriminate true cases. A nomogram to predict a bladder cancer diagnosis was created.

RESULTS

In 4,178 patients split into training and validation cohorts, those diagnosed with bladder cancer were shown to be older, have a greater degree of MH (more RBC/hpf), and were former or current smokers. A nomogram created using this model was able to predict risk of a bladder cancer diagnosis with good discrimination (areas under the curve 0.79, 95% CI 0.75-0.83). A cutoff of 0.01 probability demonstrated a sensitivity of 99.1% and a negative predictive value of 99.7%.

CONCLUSION

A nomogram can accurately predict the risk of bladder cancer diagnosed during the evaluation of MH and can potentially be used avoid a significant number of work ups in those at the lowest risk.

Individualized Prediction Of Stroke-Associated Pneumonia For Patients With Acute Ischemic Stroke

Background

Stroke-associated pneumonia (SAP) is a serious and common complication in stroke patients.

Purpose

We aimed to develop and validate an easy-to-use model for predicting the risk of SAP in acute ischemic stroke (AIS) patients.

Patients and methods

The nomogram was established by univariate and multivariate binary logistic analyses in a training cohort of 643 AIS patients. The prediction performance was determined based on the receiver operating characteristic curve (ROC) and calibration plots in a validation cohort (N=340). Individualized clinical decision-making was conducted by weighing the net benefit in each AIS patient by decision curve analysis (DCA).

Results

Seven predictors, including age, NIHSS score on admission, atrial fibrillation, nasogastric tube intervention, mechanical ventilation, fibrinogen, and leukocyte count were incorporated to construct the nomogram model. The nomogram showed good predictive performance in ROC analysis [AUROC of 0.845 (95% CI: 0.814-0.872) in training cohort, and 0.897 (95% CI: 0.860-0.927) in validation cohort], and was superior to the A²DS², ISAN, and PANTHERIS scores. Furthermore, the calibration plots showed good agreement between actual and nomogram-predicted SAP probabilities, in both training and validation cohorts. The DCA confirmed that the SAP nomogram was clinically useful.

Conclusion

Our nomogram may provide clinicians with a simple and reliable tool for predicting SAP based on routinely available data. It may also assist clinicians with respect to individualized treatment decision-making for patients differing in risk level.

Asymptomatic microscopic hematuria in women

PURPOSE OF REVIEW

The purpose of this review is to summarize the problem of asymptomatic microscopic hematuria (AMH) in women and the most recent publications on the topic.

RECENT FINDINGS

Urologic malignancy is rarely associated with AMH in low-risk women. Screening for urologic malignancy includes upper urinary tract imaging and cystoscopy. Renal ultrasound is a cost-effective first-line imaging modality in patients with AMH. Multiphasic computed tomography (CT) urography increases healthcare costs, the risk of secondary malignancy due to cumulative radiation exposure, and the discovery of incidental benign findings resulting in additional work-up. Cystoscopy is universally recommended as a diagnostic test in the evaluation of AMH but it is not without harm. Reliable risk factors for urologic malignancy in women are age, smoking, and possibly the presence of visible blood in the urine. Given the infrequency of these cancers and the performance characteristics of diagnostic testing in this context there is a need for better diagnostic strategies incorporating these risk factors in estimating the woman's risk.

SUMMARY

There is a need for sex-specific guidelines to risk stratify diagnostic evaluation for urologic malignancy in women with AMH. The low prevalence of these malignancies in women render diagnostic testing (e.g., cystoscopy and multiphasic CT urography) less impactful and pose unwarranted risk and significant healthcare costs.

Quality of the diagnostic process in patients presenting with symptoms suggestive of bladder or kidney cancer: a systematic review

OBJECTIVES

In urological cancers, sex disparity exists for survival, with women doing worse than men. Suboptimal evaluation of presenting symptoms may contribute.

DESIGN

We performed a systematic review examining factors affecting the quality of the diagnostic process of patients presenting with symptoms of bladder or kidney cancer.

DATA SOURCES

We searched Medline, Embase and the Cochrane Library from 1 January 2000 to 13 June 2019.

ELIGIBLE CRITERIA

We focused on one of the six domains of quality of healthcare: timeliness, and examined the quality of the diagnostic process more broadly, by assessing whether guideline-concordant history, examination, tests and referrals were performed. Studies describing the factors that affect the timeliness or quality of the assessment of urinary tract infections, haematuria and lower urinary tract symptoms in the context of bladder or kidney cancer, were included.

DATA EXTRACTION AND SYNTHESIS

Data extraction and quality assessment were independently performed by two authors. Due to the heterogeneity of study design and outcomes, the results could not be pooled. A narrative synthesis was performed.

RESULTS

28 studies met review criteria, representing 583 636 people from 9 high-income countries. Studies were based in primary care (n=8), specialty care (n=12), or both (n=8). Up to two-thirds of patients with haematuria received no further evaluation in the 6 months after their initial visit. Urinary tract infections, nephrolithiasis and benign prostatic conditions before cancer diagnosis were associated with diagnostic delay. Women were more likely to experience diagnostic delay than men. Patients who first saw a urologist were less likely to experience delayed evaluation and cancer diagnosis.

CONCLUSIONS

Women, and patients with non-cancerous urological diagnoses just prior to their cancer diagnosis, were more likely to experience lower quality diagnostic processes. Risk prediction tools, and improving guideline ambiguity, may improve outcomes and reduce sex disparity in survival for these cancers.

Evaluation of Hematuria in a Large Public Health Care System

Background:

Hematuria is the most common presenting symptom in bladder cancer, but many patients are not adequately evaluated.

Objectives:

To evaluate the type and frequency of hematuria evaluation in a large public health care system.

Patients and Methods:

Electronic medical records of adult patients with urinalysis positive for hematuria (≥3 RBCs/HPF) from January 2015 to April 2018 in an outpatient setting were reviewed. Logistic regression was performed to determine factors associated with urology referral and complete evaluation.

Results:

11,422 patients met the inclusion criteria; the majority were females (72%) and white race (60%). There were an additional 3,221 patient’s with initial diagnosis of UTI. Median age was 49.0 years. Testing included repeat urinalysis (50%), imaging (26%), urology referral (11.4%), cystoscopy (4.4%) and complete evaluation defined as cystoscopy and US/CT/MRI (4%). In the multivariable analysis, factors independently associated with higher referral to urology were age >35, male gender, hypertension, RBCs ≥20. African American race was associated with less referral to urology. Smoking was a significant variable on univariable analysis only. 37 patients (0.25%) were diagnosed with urological malignancies, with bladder cancer in 33, 12 of whom are missed by excluding UTI patients.

Conclusions:

In the outpatient setting of a public health care system, the vast majority of patients with hematuria are not referred and evaluated properly across all age categories and regardless of smoking status. This might result in missed cancer diagnoses and requires quality improvement measures.

Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer

BACKGROUND

A lack of consensus exists amongst national guidelines regarding who should be investigated for haematuria. Type of haematuria and age-specific thresholds are frequently used to guide referral for the investigation of haematuria.

OBJECTIVES

To develop and externally validate the haematuria cancer risk score (HCRS) to improve patient selection for the investigation of haematuria.

METHODS

Development cohort comprise of 3539 prospectively recruited patients recruited at 40 UK hospitals (DETECT 1; ClinicalTrials.gov: NCT02676180) and validation cohort comprise of 656 Swiss patients. All patients were aged >18 years and referred to hospital for the evaluation of visible and nonvisible haematuria. Sensitivity and specificity of the HCRS in the validation cohort were derived from a cut-off identified from the discovery cohort.

RESULTS

Patient age, gender, type of haematuria and smoking history were used to develop the HCRS. HCRS validation achieves good discrimination (AUC 0.835; 95% CI: 0.789-0.880) and calibration (calibration slope = 1.215) with no significant overfitting (P = 0.151). The HCRS detected 11.4% (n = 8) more cancers which would be missed by UK National Institute for Health and Clinical Excellence guidelines. The American Urological Association guidelines would identify all cancers with a specificity of 12.6% compared to 30.5% achieved by the HCRS. All patients with upper tract cancers would have been identified.

CONCLUSION

The HCRS offers good discriminatory accuracy which is superior to existing guidelines. The simplicity of the model would facilitate adoption and improve patient and physician decision-making.