scholarly journals Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

2019 ◽  
pp. 1-10
Author(s):  
Gabriel A. Brooks ◽  
Hajime Uno ◽  
Erin J. Aiello Bowles ◽  
Alexander R. Menter ◽  
Maureen O’Keeffe-Rosetti ◽  
...  

PURPOSE Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients. PATIENTS AND METHODS We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%). RESULTS There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group. CONCLUSION A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.

2019 ◽  
Vol 15 (33) ◽  
pp. 3783-3795
Author(s):  
Zhen Zhang ◽  
Rowan G Bullock ◽  
Herbert Fritsche

Aims: Adnexal mass risk assessment (AMRA) stratifies patients with adnexal masses, identifying the relatively small number of malignancies from benigns which might take a ‘watchful waiting’ approach. Methods: AMRA uses seven biomarkers and derived from women with adnexal masses scheduled for surgery. Estimated clinical performance was calculated using fixed prevalence. Results: At 5% prevalence, the high-risk group, 7.9% total, captured 75.9% of invasive malignancies at a positive predictive value of 35.8%. High risk/intermediate risk combined had a sensitivity of 89.7 and 95.6% for pre- and post-menopausal cancers, respectively. The low-risk group, 67.8% total, had an negative predictive value of 99.0%. Conclusion: With highly differentiating risk stratification capability across histological subtypes and stages, AMRA is potentially applicable to patients with adnexal masses to assist deciding whether immediate surgery is recommended.


2016 ◽  
Vol 48 (3) ◽  
pp. 780-786 ◽  
Author(s):  
Cecilia Becattini ◽  
Giancarlo Agnelli ◽  
Mareike Lankeit ◽  
Luca Masotti ◽  
Piotr Pruszczyk ◽  
...  

The European Society of Cardiology (ESC) has proposed an updated risk stratification model for death in patients with acute pulmonary embolism based on clinical scores (Pulmonary Embolism Severity Index (PESI) or simplified PESI (sPESI)), right ventricle dysfunction (RVD) and elevated serum troponin (2014 ESC model).We assessed the ability of the 2014 ESC model to predict 30-day death after acute pulmonary embolism. Consecutive patients with symptomatic, confirmed pulmonary embolism included in prospective cohorts were merged in a collaborative database. Patients’ risk was classified as high (shock or hypotension), intermediate-high (RVD and elevated troponin), intermediate-low (RVD or increased troponin or none) and low (sPESI 0). Study outcomes were death and pulmonary embolism-related death at 30 days.Among 906 patients (mean±sd age 68±16, 489 females), death and pulmonary embolism-related death occurred in 7.2% and 4.1%, respectively. Death rate was 22% in “high-risk” (95% CI 14.0–29.8), 7.7% in “intermediate-high-risk” (95% CI 4.5–10.9) and 6.0% in “intermediate-low-risk” patients (95% CI 3.4–8.6). One of the 196 “low-risk” patients died (0.5%, 95% CI 0–1.0; negative predictive value 99.5%).By using the 2014 ESC model, RVD or troponin tests would be avoided in about 20% of patients (sPESI 0), preserving a high negative predictive value. Risk stratification in patients at intermediate risk requires further improvement.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2075-2075
Author(s):  
Ryotaro Nakamura ◽  
Anna Israyelyan ◽  
Leanne Goldstein ◽  
Weimin Tsai ◽  
Lia Aquino ◽  
...  

Abstract Relapse is the major cause of treatment failure after allogeneic hematopoietic cell transplantation (HCT) for leukemia and myelodysplastic syndrome (MDS). In order to improve the outcome of leukemia patients after allogeneic HCT, it is imperative to identify reliable markers to predict impending relapse. Wilms’ tumor antigen (WT1) is overexpressed in the majority of leukemia and MDS patients and is being considered as a possible universal diagnostic marker for minimal residual disease (MRD), especially since no chromosomal translocation has nearly the frequency of association as WT1 does with leukemia. In this study we prospectively evaluated the prognostic value of MRD monitoring by qRT-PCR for WT1 transcripts. WT1 transcript levels in peripheral blood mononuclear cells (PBMC) were measured utilizing SYBR-Green qRT-PCR on the ABI7300 instrument (Applied Biosystems, Carlsbad, CA) and results were expressed as a ratio of WT1/c-ABL transcript copies normalized by 104 (WT1 ratio: WT1/c-ABLx104). PBMC samples were obtained monthly for 6 months post-HCT, then on alternating monthly schedule until 3 years post-HCT. Patients >18y.o. with confirmed diagnosis of MDS with <20% blasts, AML/ALL in 1st or 2ndCR, and CML in chronic phase undergoing HCT were eligible for the study. A total of 83 patients (median age: 54, range: 19-74) with AML (n=39), ALL (n=24), MDS (n=17), or CML (n=3) received allogeneic HCT after fully ablative (n=39) or reduced-intensity (n=44) conditioning. Donor sources were matched related (n=33), unrelated (n=50), or umbilical cord blood (n=2). Fifty-one patients were considered low-risk (AML/ALL in CR1, CML in 1stchronic phase, or MDS-RA/RARS subtypes) while the remaining 32 patients were considered high risk. Sixteen of 83 patients relapsed with a median time of 238 days post-HCT (range: 76-747). The minimum WT1 ratio that gave specificity of 100% in predicting relapse was 50 (95% binomial exact CI: 92.5-100%), as none of the non-relapsed patients crossed this level. Of 16 patients who relapsed, 12 crossed the WT1 ratio of 50, providing a sensitivity of 75% (95% binomial exact CI: 48- 93%). The positive predictive value (PPV) and the negative predictive value (NPV) performance parameters for the WT1 ratio of 50 were 100% and 94.4%, respectively (Table 1). There was an average number of 63 days (SD=29.3) from crossing the WT1 ratio of 50 to hematologic relapse for the 12 relapsed patients. Since PBMC samples from healthy donors consistently demonstrated a WT1 ratio <10, we also examined different WT1 cutoff ratios (10 to 50) for their performance characteristics (Table 1). Compared with the WT1 ratio of 50, a cutoff ratio at 20 resulted in an increased sensitivity (87.5%) for relapse prediction and days to relapse (78 days), while the specificity and PPV decreased to 85% and 58.3%, respectively. The performance of the cutoff ratio of 20 was improved on PPV (69%) and days to relapse (85 days) in a subgroup of patients with high-risk disease while maintaining good sensitivity and specificity above 80%. Univariate analysis showed WT1 ratios (as a continuous variable), crossing the WT1 ratio of 20 (as a time-dependent variable), high risk disease, and donor age were significantly associated with relapse. In multivariate analysis, crossing the WT1 ratio of 20 remained the only significant factor predicting relapse (HR 56.9 [18-189], p<0.0001). In summary, our data demonstrate that the quantitative measurement of WT1 transcripts is a reliable marker to assess MRD post-HCT for leukemia patients, and its real-time prospective monitoring provides a 2-3 month window of opportunity to introduce medical/immunologic interventions prior to overt hematologic relapse.Table 2WT1 ratio Specificity Sensitivity Positive Predictive Value Negative Predictive Value Days to Relapse (SD)50100%75%100%94.4%63 days (29)4095.5%75%80%94.1%66.9 days (29)3094%75%75%94%71.5 days (30)2085%87.5%58.3%96.6%78 days (28)1056.7%93.8%34.1%97.4%107.5 days (58) Disclosures: No relevant conflicts of interest to declare.


2019 ◽  
Vol 78 (10) ◽  
pp. 1412-1419 ◽  
Author(s):  
Leena Sharma ◽  
Kent Kwoh ◽  
Jungwha (Julia) Lee ◽  
Jane Cauley ◽  
Rebecca Jackson ◽  
...  

ObjectivesDisability prevention strategies are more achievable before osteoarthritis disease drives impairment. It is critical to identify high-risk groups, for strategy implementation and trial eligibility. An established measure, gait speed is associated with disability and mortality. We sought to develop and validate risk stratification trees for incident slow gait in persons at high risk for knee osteoarthritis, feasible in community and clinical settings.MethodsOsteoarthritis Initiative (derivation cohort) and Multicenter Osteoarthritis Study (validation cohort) participants at high risk for knee osteoarthritis were included. Outcome was incident slow gait over up to 10-year follow-up. Derivation cohort classification and regression tree analysis identified predictors from easily assessed variables and developed risk stratification models, then applied to the validation cohort. Logistic regression compared risk group predictive values; area under the receiver operating characteristic curves (AUCs) summarised discrimination ability.Results1870 (derivation) and 1279 (validation) persons were included. The most parsimonious tree identified three risk groups, from stratification based on age and WOMAC Function. A 7-risk-group tree also included education, strenuous sport/recreational activity, obesity and depressive symptoms; outcome occurred in 11%, varying 0%–29 % (derivation) and 2%–23 % (validation) depending on risk group. AUCs were comparable in the two cohorts (7-risk-group tree, 0.75, 95% CI 0.72 to 0.78 (derivation); 0.72, 95% CI 0.68 to 0.76 (validation)).ConclusionsIn persons at high risk for knee osteoarthritis, easily acquired data can be used to identify those at high risk of incident functional impairment. Outcome risk varied greatly depending on tree-based risk group membership. These trees can inform individual awareness of risk for impaired function and define eligibility for prevention trials.


2001 ◽  
Vol 22 (08) ◽  
pp. 481-484 ◽  
Author(s):  
M. Sigfrido Rangel-Frausto ◽  
Samuel Ponce-de-León-Rosales ◽  
Claudia Martinez-Abaroa ◽  
Kaare Hasløv

Abstract Objective: To compare the performance of three purified protein derivative (PPD) formulations: Tubersol (Connaught); RT23, Statens Serum Institut (SSI); and RT23, Mexico, tested in Mexican populations at low and high risk for tuberculosis (TB). Design: A double-blinded clinical trial. Setting: A university hospital in Mexico City. Participants: The low-risk population was first or second-year medical students with no patient contact; the high-risk population was healthcare workers at a university hospital. Methods: Each of the study subjects received the three different PPD preparations. Risk factors for TB, including age, gender, occupation, bacille Calmette-Guerin (BCG) status, and TB exposure, were recorded. A 0.1-mL aliquot of each preparation was injected in the left and right forearms of volunteers using the Mantoux technique. Blind readings were done 48 to 72 hours later. Sensitivity and specificity were calculated at 10 mm of induration using Tubersol as the reference standard. The SSI tested the potency of the different PPD preparations in previously sensitized guinea pigs. Results: The low-risk population had a prevalence of positive PPD of 26%. In the low-risk population, RT23 prepared in Mexico, compared to the 5 TU of Tubersol, had a sensitivity of 51%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 86%. The RT23 prepared at the SSI had a sensitivity of 69%, a specificity of 99%, a positive predictive value of 95%, and a negative predictive value of 90%. In the high-risk population, the prevalence of positive PPD was 57%. The RT23 prepared in Mexico had a sensitivity of 33%, a specificity of 100%, and a positive predictive value of 53%; the RT23 prepared at the SSI had a sensitivity of 91%, a specificity of 98%, a positive predictive value of 98%, and a negative predictive value of 89%. RT23 used in Mexico had a potency of only 23% of that of the control. There was no statistical association among those with a positive PPD, irrespective of previous BCG vaccination (relative risk, 0.97; 95% confidence interval, 0.76-1.3; P=.78). Conclusions: Healthcare workers had twice the prevalence of positive PPD compared to medical students. RT23 prepared in Mexico had a low sensitivity in both populations compared to 5 TU of Tubersol and RT23 prepared at the SSI. Previous BCG vaccination did not correlate with a positive PPD. Low potency of the RT23 preparation in Mexico was confirmed in guinea pigs. Best intentions in a TB program are not enough if they are not followed by high-quality control.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 750-750
Author(s):  
Ang Li ◽  
Kylee L Martens ◽  
Daniel Nguyen ◽  
Gabriela Rondon ◽  
Christopher I Amos ◽  
...  

Abstract Introduction: In patients undergoing allogeneic hematopoietic cell transplantation (HCT), venous thromboembolism (VTE) remains a serious complication that lacks validated risk assessment models to guide optimal timing and implementation of thromboprophylaxis. We recently derived the HIGH-2-LOW score that incorporated 7 simple clinical predictors assessed at day 30 post-transplant (Table 1, PMID 33570631). In this present study, we performed validation in two independent datasets. Methods: We selected consecutive patients undergoing first allogeneic HCT from Fred Hutchinson Cancer Research Center (FHCRC, 2015-2019) and MD Anderson Cancer Center (MDACC, 2016-2020). Patients who died, received therapeutic anticoagulation, or did not engraft platelets at day 30 were excluded (Table 2). Day 30 was chosen as the index date because most patients would be transfusion-dependent before that time. We used a combination of ICD9/10 codes and natural language processing (NLP) algorithms to identify possible cases of VTE, followed by confirmation via individual chart review. VTE was defined as radiology-confirmed pulmonary embolism (PE), lower-extremity deep venous thromboembolism (LE-DVT), or catheter-related DVT (CR-DVT). Covariates were captured and weighted according to the original model, except that grade 2-4 was used instead of grade 3-4 GVHD. Discrimination was assessed in each cohort by fitting logistic regression models with VTE and PE/LE-DVT outcomes at 100 days to estimate the c statistic, where a higher c statistic is desirable. Both continuous scores and categorical models were assessed. Kaplan Meier failure curves were plotted to compare the final risk stratification with high- vs. low/intermediate-risk groups. Results: The two cohorts (n=772 in FHCRC, n=1109 in MDACC) had similar characteristics in age, sex, race, weight, disease, and conditioning intensity. Key differences between the two cohorts included a higher number of umbilical cord transplants at FHCRC (vs. haploidentical at MDACC), higher numbers of acute GVHD at FHCRC (due to differences in grading criteria), fewer historical CR-DVT at FHCRC (4.0% vs. 6.8%), and more anticoagulation treatment at day 30 at FHCRC (9.0% vs. 3.5% - excluded). Incident VTE was 2.5% by 100 days and 7.8% by 365 days at FHCRC; incident VTE was 5.4% by 100 days and 9.4% by 365 days at MDACC (Table 2). Incident PE or LE-DVTs were similar in the two cohorts. When treated as a continuous score, every 1-point increase in the HIGH-2-LOW score was associated with odds ratio (OR) of 1.55 for VTE (1.06-2.27, c=0.64) and 2.50 (1.40-4.44, c=0.84) for PE/LE-DVT in the FHCRC cohort. The same increase was associated with OR of 1.93 (1.55-2.39, c=0.64) for VTE and 2.46 (1.61-3.76, c=0.79) for PE/LE-DVT in the MDACC cohort (Table 3). A total of 24% and 19% of patients were classified as high-risk (2+ points), respectively. High vs. low/intermediate-risk was associated with OR of 2.99 (1.20-7.49, c=0.62) for VTE and 19.93 (2.38-166.67, c=0.81) for PE/LE-DVT in the internal validation cohort. High vs. low/intermediate-risk was associated with OR of 4.58 (2.69-7.79, c=0.66) for VTE and 12.05 (3.17-45.81, c=0.77) for PE-LE-DVT in the external validation cohort. The VTE risk stratification separated early and persisted beyond 100 days (Figure 1). Conclusion: Despite differences in HCT and patient characteristics, the HIGH-2-LOW score identified ~20% of allogeneic HCT recipients at high-risk for VTE, particularly that of PE or LE-DVT, in both independent validation cohorts. The lower-than-expected absolute VTE incidence in the FHCRC cohort was likely driven by the increasing use of anticoagulation immediately post-transplant (exclusion criteria); however, the model retained similar OR with modest discrimination in both cohorts. In patients with prior history of PE/LE-DVT off anticoagulation, or those with prolonged admission and at least 1 additional risk factors from the HIGH-2-LOW score (2+ points), VTE prophylaxis should be considered upon platelet engraftment. Figure 1 Figure 1. Disclosures Lee: Kadmon: Research Funding; Syndax: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Novartis: Other: clinical trials, Research Funding; JANSSEN: Other; Incyte: Research Funding; AstraZeneca: Research Funding; Amgen: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees. Shpall: Bayer HealthCare Pharmaceuticals: Honoraria; Novartis: Consultancy; Takeda: Patents & Royalties; Affimed: Patents & Royalties; Magenta: Honoraria; Magenta: Consultancy; Navan: Consultancy; Novartis: Honoraria; Axio: Consultancy; Adaptimmune: Consultancy.


2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Wang ◽  
Yue Zhao ◽  
Bingyu Jin ◽  
Xuedong Gan ◽  
Bin Liang ◽  
...  

Early identification of coronary artery disease (CAD) can prevent the progress of CAD and effectually lower the mortality rate, so we intended to construct and validate a machine learning model to predict the risk of CAD based on conventional risk factors and lab test data. There were 3,112 CAD patients and 3,182 controls enrolled from three centers in China. We compared the baseline and clinical characteristics between two groups. Then, Random Forest algorithm was used to construct a model to predict CAD and the model was assessed by receiver operating characteristic (ROC) curve. In the development cohort, the Random Forest model showed a good AUC 0.948 (95%CI: 0.941–0.954) to identify CAD patients from controls, with a sensitivity of 90%, a specificity of 85.4%, a positive predictive value of 0.863 and a negative predictive value of 0.894. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value, and negative predictive value of 0.944 (95%CI: 0.934–0.955), 89.5%, 85.8%, 0.868, and 0.886 in the validation cohort 1, respectively, and 0.940 (95%CI: 0.922–0.960), 79.5%, 94.3%, 0.932, and 0.823 in the validation cohort 2, respectively. An easy-to-use tool that combined 15 indexes to assess the CAD risk was constructed and validated using Random Forest algorithm, which showed favorable predictive capability (http://45.32.120.149:3000/randomforest). Our model is extremely valuable for clinical practice, which will be helpful for the management and primary prevention of CAD patients.


2020 ◽  
Author(s):  
Sen Li ◽  
Wenpeng Wang ◽  
Pengfei Ma ◽  
Junli Zhang ◽  
Yanghui Cao ◽  
...  

Abstract Background In order to accurately predict outcomes of gastric cancer (GC), we developed a risk signature with tumor infiltration immune and inflammatory cells for prognosis.Methods A risk signature model in combination with CD66b + neutrophils, CD3 + T, CD8 + T lymphocytes, and FOXP3 + regulatory T cells was developed in a training cohort of 327 GC patients undergoing surgical resection between 2011 and 2012, and validated in a validation cohort of 285 patients from 2012 to 2013.Results High CD66b expression predicted poor disease-special survival (DSS) as well as inversely correlated with CD8 (P < 0.05) and FOXP3 expression (P < 0.05) in the training cohort, comparable disease-free survival (DFS) findings were observed in the validation cohort.Furthermore, a risk stratification was developed from integration of CD66b + neutrophils and T immune cells. In both DFS and DSS, the high-risk group all demonstrated worse prognosis than low-risk group in both the training cohort and the validation cohort (all P < 0.05). In addition, the high-risk group was associated with post-operative relapses. Furthermore, this risk signature model increase the predictive accuracy and efficiency for post-operative relapses. At last, the high-risk group identified a subgroup of GC patients who tend to not benefit from adjuvant chemotherapy.Conclusions Incorporation of neutrophils into T lymphocytes could provide more accurate prognostic information in GC, and this risk stratification predicted survival benefit from post-operative adjuvant chemotherapy in GC.


2019 ◽  
Vol 7 (1) ◽  
pp. e000769 ◽  
Author(s):  
Anne Jølle ◽  
Kristian Midthjell ◽  
Jostein Holmen ◽  
Sven Magnus Carlsen ◽  
Jaakko Tuomilehto ◽  
...  

ObjectiveThe Finnish Diabetes Risk Score (FINDRISC) is a recommended tool for type 2 diabetes prediction. There is a lack of studies examining the performance of the current 0–26 point FINDRISC scale. We examined the validity of FINDRISC in a contemporary Norwegian risk environment.Research design and methodsWe followed 47 804 participants without known diabetes and aged ≥20 years in the HUNT3 survey (2006–2008) by linkage to information on glucose-lowering drug dispensing in the Norwegian Prescription Database (2004–2016). We estimated the C-statistic, sensitivity and specificity of FINDRISC as predictor of incident diabetes, as indicated by incident use of glucose-lowering drugs. We estimated the 10-year cumulative diabetes incidence by categories of FINDRISC.ResultsThe C-statistic (95% CI) of FINDRISC in predicting future diabetes was 0.77 (0.76 to 0.78). FINDRISC ≥15 (the conventional cut-off value) had a sensitivity of 38% and a specificity of 90%. The 10-year cumulative diabetes incidence (95% CI) was 4.0% (3.8% to 4.2%) in the entire study population, 13.5% (12.5% to 14.5%) for people with FINDRISC ≥15 and 2.8% (2.6% to 3.0%) for people with FINDRISC <15. Thus, FINDRISC ≥15 had a positive predictive value of 13.5% and a negative predictive value of 97.2% for diabetes within the next 10 years. To approach a similar sensitivity as in the study in which FINDRISC was developed, we would have to lower the cut-off value for elevated FINDRISC to ≥11. This would yield a sensitivity of 73%, specificity of 67%, positive predictive value of 7.7% and negative predictive value of 98.5%.ConclusionsThe validity of FINDRISC and the risk of diabetes among people with FINDRISC ≥15 is substantially lower in the contemporary Norwegian population than assumed in official guidelines. To identify ~3/4 of those developing diabetes within the next 10 years, we would have to lower the threshold for elevated FINDRISC to ≥11, which would label ~1/3 of the entire adult population as having an elevated FINDRISC necessitating a glycemia assessment.


2016 ◽  
Vol 116 (08) ◽  
pp. 396-396

In the Original Article “The SAME-TT2R2 score predicts the quality of anticoagulation control in patients with acute VTE. A real-life inception cohort study” (Thromb Haemost 2016; 115: 1101-1108) by Palareti et al. the last sentence at the end of the second paragraph on page 1105 of the article is wrong and should read as follows: “Regarding the ability of the score ≥2 to predict a TTR <65%, the C statistic was 0.52 (95% CI 0.48–0.55; p = 0.35); the sensitivity was 74% (70.4–77.8), specificity 29% (25.3–33.2), negative predictive value 52% (95% CI 45.8–57.7) and positive predictive value 53% (40.0–56.2).” The authors apologise for this error.


Sign in / Sign up

Export Citation Format

Share Document