Dataset Information

Derivation and external validation of a risk score for predicting HIV-associated tuberculosis to support case finding and preventive therapy scale-up: A cohort study.

ABSTRACT:

Background

Among people living with HIV (PLHIV), more flexible and sensitive tuberculosis (TB) screening tools capable of detecting both symptomatic and subclinical active TB are needed to (1) reduce morbidity and mortality from undiagnosed TB; (2) facilitate scale-up of tuberculosis preventive therapy (TPT) while reducing inappropriate prescription of TPT to PLHIV with subclinical active TB; and (3) allow for differentiated HIV-TB care.

Methods and findings

We used Botswana XPRES trial data for adult HIV clinic enrollees collected during 2012 to 2015 to develop a parsimonious multivariable prognostic model for active prevalent TB using both logistic regression and random forest machine learning approaches. A clinical score was derived by rescaling final model coefficients. The clinical score was developed using southern Botswana XPRES data and its accuracy validated internally, using northern Botswana data, and externally using 3 diverse cohorts of antiretroviral therapy (ART)-naive and ART-experienced PLHIV enrolled in XPHACTOR, TB Fast Track (TBFT), and Gugulethu studies from South Africa (SA). Predictive accuracy of the clinical score was compared with the World Health Organization (WHO) 4-symptom TB screen. Among 5,418 XPRES enrollees, 2,771 were included in the derivation dataset; 67% were female, median age was 34 years, median CD4 was 240 cells/μL, 189 (7%) had undiagnosed prevalent TB, and characteristics were similar between internal derivation and validation datasets. Among XPHACTOR, TBFT, and Gugulethu cohorts, median CD4 was 400, 73, and 167 cells/μL, and prevalence of TB was 5%, 10%, and 18%, respectively. Factors predictive of TB in the derivation dataset and selected for the clinical score included male sex (1 point), ≥1 WHO TB symptom (7 points), smoking history (1 point), temperature >37.5°C (6 points), body mass index (BMI) <18.5kg/m2 (2 points), and severe anemia (hemoglobin <8g/dL) (3 points). Sensitivity using WHO 4-symptom TB screen was 73%, 80%, 94%, and 94% in XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively, but increased to 88%, 87%, 97%, and 97%, when a clinical score of ≥2 was used. Negative predictive value (NPV) also increased 1%, 0.3%, 1.6%, and 1.7% in XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively, when the clinical score of ≥2 replaced WHO 4-symptom TB screen. Categorizing risk scores into low (<2), moderate (2 to 10), and high-risk categories (>10) yielded TB prevalence of 1%, 1%, 2%, and 6% in the lowest risk group and 33%, 22%, 26%, and 32% in the highest risk group for XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively. At clinical score ≥2, the number needed to screen (NNS) ranged from 5.0 in Gugulethu to 11.0 in XPHACTOR. Limitations include that the risk score has not been validated in resource-rich settings and needs further evaluation and validation in contemporary cohorts in Africa and other resource-constrained settings.

Conclusions

The simple and feasible clinical score allowed for prioritization of sensitivity and NPV, which could facilitate reductions in mortality from undiagnosed TB and safer administration of TPT during proposed global scale-up efforts. Differentiation of risk by clinical score cutoff allows flexibility in designing differentiated HIV-TB care to maximize impact of available resources.

SUBMITTER: Auld AF

PROVIDER: S-EPMC8454974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe prevalence of and mortality from HIV-associated tuberculosis (HIV/TB) in hospital inpatients in Africa remains unacceptably high. Currently, there is a lack of tools to identify those at high risk of early mortality who may benefit from adjunctive interventions. We therefore aimed to develop and validate a simple clinical risk score to predict mortality in high-burden, low-resource settings.Methods and findingsA cohort of HIV-positive adults with laboratory-confirmed TB from the STAMP TB screening trial (Malawi and South Africa) was used to derive a clinical risk score using multivariable predictive modelling, considering factors at hospital admission (including urine lipoarabinomannan [LAM] detection) thought to be associated with 2-month mortality. Performance was evaluated internally and then externally validated using independent cohorts from 2 other studies (LAM-RCT and a Médecins Sans Frontières [MSF] cohort) from South Africa, Zambia, Zimbabwe, Tanzania, and Kenya. The derivation cohort included 315 patients enrolled from October 2015 and September 2017. Their median age was 36 years (IQR 30-43), 45.4% were female, median CD4 cell count at admission was 76 cells/μl (IQR 23-206), and 80.2% (210/262) of those who knew they were HIV-positive at hospital admission were taking antiretroviral therapy (ART). Two-month mortality was 30% (94/315), and mortality was associated with the following factors included in the score: age 55 years or older, male sex, being ART experienced, having severe anaemia (haemoglobin < 80 g/l), being unable to walk unaided, and having a positive urinary Determine TB LAM Ag test (Alere). The score identified patients with a 46.4% (95% CI 37.8%-55.2%) mortality risk in the high-risk group compared to 12.5% (95% CI 5.7%-25.4%) in the low-risk group (p < 0.001). The odds ratio (OR) for mortality was 6.1 (95% CI 2.4-15.2) in high-risk patients compared to low-risk patients (p < 0.001). Discrimination (c-statistic 0.70, 95% CI 0.63-0.76) and calibration (Hosmer-Lemeshow statistic, p = 0.78) were good in the derivation cohort, and similar in the external validation cohort (complete cases n = 372, c-statistic 0.68 [95% CI 0.61-0.74]). The validation cohort included 644 patients between January 2013 and August 2015. Median age was 36 years, 48.9% were female, and median CD4 count at admission was 61 (IQR 21-145). OR for mortality was 5.3 (95% CI 2.2-9.5) for high compared to low-risk patients (complete cases n = 372, p < 0.001). The score also predicted patients at higher risk of death both pre- and post-discharge. A simplified score (any 3 or more of the predictors) performed equally well. The main limitations of the scores were their imperfect accuracy, the need for access to urine LAM testing, modest study size, and not measuring all potential predictors of mortality (e.g., tuberculosis drug resistance).ConclusionsThis risk score is capable of identifying patients who could benefit from enhanced clinical care, follow-up, and/or adjunctive interventions, although further prospective validation studies are necessary. Given the scale of HIV/TB morbidity and mortality in African hospitals, better prognostic tools along with interventions could contribute towards global targets to reduce tuberculosis mortality.

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Project description:ObjectiveTo develop and externally validate a prediction model for severe cisplatin associated acute kidney injury (CP-AKI).DesignMulticenter cohort study.SettingSix geographically diverse major academic cancer centers across the US.ParticipantsAdults (≥18 years) receiving their first dose of intravenous cisplatin, 2006-22.Main outcome measuresThe primary outcome was CP-AKI, defined as a twofold or greater increase in serum creatinine or kidney replacement therapy within 14 days of a first dose of intravenous cisplatin. Independent predictors of CP-AKI were identified using a multivariable logistic regression model, which was developed in a derivation cohort and tested in an external validation cohort. For the primary model, continuous variables were examined using restricted cubic splines. A simple risk model was also generated by converting the odds ratios from the primary model into risk points. Finally, a multivariable Cox model was used to examine the association between severity of CP-AKI and 90 day survival.ResultsA total of 24 717 adults were included, with 11 766 in the derivation cohort (median age 59 (interquartile range (IQR) 50-67)) and 12 951 in the validation cohort (median age 60 (IQR 50-67)). The incidence of CP-AKI was 5.2% (608/11 766) in the derivation cohort and 3.3% (421/12 951) in the validation cohort. Each of the following factors were independently associated with CP-AKI in the derivation cohort: age, hypertension, diabetes mellitus, serum creatinine level, hemoglobin level, white blood cell count, platelet count, serum albumin level, serum magnesium level, and cisplatin dose. A simple risk score consisting of nine covariates was shown to predict a higher risk of CP-AKI in a monotonic fashion in both the derivation cohort and the validation cohort. Compared with patients in the lowest risk category, those in the highest risk category showed a 24.00-fold (95% confidence interval (CI) 13.49-fold to 42.78-fold) higher odds of CP-AKI in the derivation cohort and a 17.87-fold (10.56-fold to 29.60-fold) higher odds in the validation cohort. The primary model had a C statistic of 0.75 and showed better discrimination for CP-AKI than previously published models, the C statistics for which ranged from 0.60 to 0.68 (DeLong P<0.001 for each comparison). Greater severity of CP-AKI was monotonically associated with shorter 90 day survival (adjusted hazard ratio 4.63 (95% CI 3.56 to 6.02) for stage 3 CP-AKI versus no CP-AKI).ConclusionThis study found that a simple risk score based on readily available variables from patients receiving intravenous cisplatin could predict the risk of severe CP-AKI, the occurrence of which is strongly associated with death.

Project description:BackgroundContacts of tuberculosis index cases are at increased risk of developing tuberculosis. Screening, preventive therapy, and surveillance for tuberculosis are underused interventions in contacts, particularly adults. We developed a score to predict risk of tuberculosis in adult contacts of tuberculosis index cases.MethodsIn 2002-06, we recruited contacts aged 15 years or older of index cases with pulmonary tuberculosis who lived in desert shanty towns in Ventanilla, Peru. We followed up contacts for tuberculosis until February, 2016. We used a Cox proportional hazards model to identify index case, contact, and household risk factors for tuberculosis from which to derive a score and classify contacts as low, medium, or high risk. We validated the score in an urban community recruited in Callao, Peru, in 2014-15.FindingsIn the derivation cohort, we identified 2017 contacts of 715 index cases, and median follow-up was 10·7 years (IQR 9·5-11·8). 178 (9%) of 2017 contacts developed tuberculosis during 19 147 person-years of follow-up (incidence 0·93 per 100 person-years, 95% CI 0·80-1·08). Risk factors for tuberculosis were body-mass index, previous tuberculosis, age, sustained exposure to the index case, the index case being in a male patient, lower community household socioeconomic position, indoor air pollution, previous tuberculosis among household members, and living in a household with a low number of windows per room. The 10-year risks of tuberculosis in the low-risk, medium-risk, and high-risk groups were, respectively, 2·8% (95% CI 1·7-4·4), 6·2% (4·8-8·1), and 20·6% (17·3-24·4). The 535 (27%) contacts classified as high risk accounted for 60% of the tuberculosis identified during follow-up. The score predicted tuberculosis independently of tuberculin skin test and index-case drug sensitivity results. In the external validation cohort, 65 (3%) of 1910 contacts developed tuberculosis during 3771 person-years of follow-up (incidence 1·7 per 100 person-years, 95% CI 1·4-2·2). The 2·5-year risks of tuberculosis in the low-risk, medium-risk, and high-risk groups were, respectively, 1·4% (95% CI 0·7-2·8), 3·9% (2·5-5·9), and 8·6%· (5·9-12·6).InterpretationOur externally validated risk score could predict and stratify 10-year risk of developing tuberculosis in adult contacts, and could be used to prioritise tuberculosis control interventions for people most likely to benefit.FundingWellcome Trust, Department for International Development Civil Society Challenge Fund, Joint Global Health Trials consortium, Bill & Melinda Gates Foundation, Imperial College National Institutes of Health Research Biomedical Research Centre, Foundation for Innovative New Diagnostics, Sir Halley Stewart Trust, WHO, TB REACH, and Innovation for Health and Development.

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