Predicting in-hospital maternal mortality in Senegal and Mali.
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ABSTRACT:
SUBMITTER: Ndour C
PROVIDER: S-EPMC3667861 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Predicting in-hospital maternal mortality in Senegal and Mali.
Ndour Cheikh C Dossou Gbété Simplice S Bru Noelle N Abrahamowicz Michal M Fauconnier Arnaud A Traoré Mamadou M Diop Aliou A Fournier Pierre P Dumont Alexandre A
PloS one 20130530 5
<h4>Objective</h4>We sought to identify predictors of in-hospital maternal mortality among women attending referral hospitals in Mali and Senegal.<h4>Methods</h4>We conducted a cross-sectional epidemiological survey using data from a cluster randomized controlled trial (QUARITE trial) in 46 referral hospitals in Mali and Senegal, during the pre-intervention period of the trial (from October 1st 2007 to October 1st 2008). We included 89,518 women who delivered in the 46 hospitals during this peri ...[more]
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Project description:ObjectiveIn the context of rapid changes regarding practices related to delivery in Africa, we assessed maternal and perinatal adverse outcomes associated with the mode of delivery in 41 referral hospitals of Mali and Senegal.Study designCross-sectional survey nested in a randomised cluster trial (1/10/2007-1/10/2008). The associations between intended mode of delivery and (i) in-hospital maternal mortality, (ii) maternal morbidity (transfusion or hysterectomy), (iii) stillbirth or neonatal death before Day 1 and (iv) neonatal death between 24 hours after birth and hospital discharge were examined. We excluded women with immediate life threatening maternal or fetal complication to avoid indication bias. The analyses were performed using hierarchical logistic mixed models with random intercept and were adjusted for women's, newborn's and hospitals' characteristics.ResultsAmong the 78,166 included women, 2.2% had a pre-labor cesarean section (CS) and 97.8% had a trial of labor. Among women with a trial of labor, 87.5% delivered vaginally and 12.5% had intrapartum CS. Pre-labor CS was associated with a marked reduction in the risk of stillbirth or neonatal death before Day 1 as compared with trial of labor (OR = 0.2 [0.16-0.36]), though we did not show that maternal mortality (OR = 0.3 [0.07-1.32]) and neonatal mortality after Day 1 (OR = 1.3 (0.66-2.72]) differed significantly between groups. Among women with trial of labor, intrapartum CS and operative vaginal delivery were associated with higher risks of maternal mortality and morbidity, and neonatal mortality after Day 1, as compared with spontaneous vaginal delivery.ConclusionsIn referral hospitals of Mali and Senegal, pre-labor CS is a safe procedure although intrapartum CS and operative vaginal delivery are associated with increased risks in mothers and infants. Further research is needed to determine what aspects of obstetric care contribute to a delay in the provision of intrapartum interventions so that practices may be made safer when they are needed.
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Project description:Aim of the studyMost survivors of an in-hospital cardiac arrest do not leave the hospital alive, and there is a need for a more patient-centered, holistic approach to the assessment of prognosis after an arrest. We sought to identify pre-, peri-, and post-arrest variables associated with in-hospital mortality amongst survivors of an in-hospital cardiac arrest.MethodsThis was a retrospective cohort study of patients ≥18 years of age who were resuscitated from an in-hospital arrest at our University Medical Center from January 1, 2013 to September 31, 2016. In-hospital mortality was chosen as a primary outcome and unfavorable discharge disposition (discharge disposition other than home or skilled nursing facility) as a secondary outcome.Results925 patients comprised the in-hospital arrest cohort with 305 patients failing to survive the arrest and a further 349 patients surviving the initial arrest but dying prior to hospital discharge, resulting in an overall survival of 29%. 620 patients with a ROSC of greater than 20 min following the in-hospital arrest were included in the final analysis. In a stepwise multivariable regression analysis, recurrent cardiac arrest, increasing age, time to ROSC, higher serum creatinine levels, and a history of cancer were predictors of in-hospital mortality. A history of hypertension was found to exert a protective effect on outcomes. In the regression model including serum lactate, increasing lactate levels were associated with lower odds of survival.ConclusionAmongst survivors of in-hospital cardiac arrest, recurrent cardiac arrest was the strongest predictor of poor outcomes with age, time to ROSC, pre-existing malignancy, and serum creatinine levels linked with increased odds of in-hospital mortality.
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Project description:BackgroundMaternal mortality is higher among Black than White people in the United States. Whether Black-White disparities in maternal in-hospital mortality during the delivery hospitalization vary across hospital types (Black-serving vs nonBlack-serving and teaching vs nonteaching) and whether overall maternal mortality differs across hospital types is not known.ObjectiveThe aims of this study were to determine whether risk-adjusted Black-White disparities in maternal mortality during the delivery hospitalization vary by hospital types (this is analysis of disparities in mortality within hospital types) and compare risk-adjusted in-hospital maternal mortality among Black-serving and nonBlack-serving teaching and nonteaching hospitals regardless of race (this is an analysis of overall mortality across hospital types).Study designWe performed a population-based, retrospective cohort study of 5,679,044 deliveries among Black (14.2%) and White patients (85.8%) in 3 states (California, Missouri, and Pennsylvania) from 1995 to 2009. A hospital discharge disposition of "death" defined maternal in-hospital mortality. Black-serving hospitals had at least 7% Black obstetrical patients (top quartile). We performed risk adjustment by calculating expected death rates using predictions from logistic regression models incorporating sociodemographics, rurality, comorbidities, multiple gestations, gestational age at delivery, year, state, and mode of delivery. We calculated risk-adjusted risk ratios of mortality by comparing observed-to-expected ratios among Black and White patients within hospital types and then examined mortality across hospital types, regardless of patient race. We quantified the proportion of Black-White disparities in mortality attributable to delivering in Black-serving hospitals using causal mediation analysis.ResultsThere were 330 maternal deaths among 5,679,044 patients (5.8 per 100,000). Black patients died more often (11.5 per 100,000) than White patients (4.8 per 100,000) (relative risk, 2.38; 95% confidence interval, 1.89-2.98). Examination of Black-White disparities revealed that after risk adjustment, Black patients had significantly greater risk of death (adjusted relative risk, 1.44; 95% confidence interval, 1.17-1.79) and that the disparity was similar within each of the hospital types. Comparison of mortality, regardless of race, across hospital types revealed that among teaching hospitals, mortality was similar in Black-serving and nonBlack-serving hospitals. However, among nonteaching hospitals, mortality was significantly higher in Black-serving vs nonBlack-serving hospitals (adjusted relative risk, 1.47; 95% confidence interval, 1.15-1.87). Notably, 53% of Black patients delivered in nonteaching, Black-serving hospitals compared with just 19% of White patients. Among nonteaching hospitals, 47% of Black-White disparities in maternal in-hospital mortality were attributable to delivering at Black-serving hospitals.ConclusionMaternal in-hospital mortality during the delivery hospitalization among Black patients is more than double that of White patients. Our data suggest this disparity is caused by excess mortality among Black patients within each hospital type, in addition to excess mortality in nonteaching, Black-serving hospitals where most Black patients deliver. Addressing downstream effects of racism to achieve equity in maternal in-hospital mortality will require transparent reporting of quality metrics by race to reduce differential care and outcomes within hospital types, improvements in care delivery at Black-serving hospitals, overcoming barriers to accessing high-quality care among Black patients, and eventually desegregation of healthcare.
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Project description:Using structured elements from Electronic Health Records (EHR), we seek to: i ) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, and ii ) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.
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Project description:Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849-0.898), NPR 0.875 (95% CI: 0.851-0.899) in training cohort and NLR 0.856 (95% CI: 0.818-0.895), NPR 0.863 (95% CI: 0.826-0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885-0.919) for VNLR and 0.891 (95% CI: 0.861-0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.
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Project description:BACKGROUND:Seasonal malaria chemoprevention is widely implemented in Sahel and sub-Sahel countries in Africa. Few studies have assessed the impact of the SMC on hospital admission and death when it is implemented in the health system. This retrospective study assessed the impact of seasonal malaria chemoprevention (SMC) on hospitalizations and deaths of children under 5 years of age during the second year of implementation of SMC in the health district of Ouelessebougou in Mali. METHODS:In February 2017, a survey was conducted to assess hospital admissions and deaths in children under 5 years of age in two health sub-districts where SMC was implemented in 2015 and two health sub-districts where SMC was not implemented. The survey reviewed deaths and hospitalizations of children under 5, in the four health sub-districts. The crude and specific incidence rates of hospitalizations and deaths were determined in both groups and expressed per 1000 children per year. A negative binomial regression model and a Cox model were used to estimate the relative risks of hospitalization and death after adjusting for confounders. The R software was used for data analysis. RESULTS:A total of 6638 children under 5 years of age were surveyed, 2759 children in the SMC intervention areas and 3879 children in the control areas. All causes mortality rate per 1000 person-years was 8.29 in the control areas compared to 3.63 in the intervention areas; age and gender adjusted mortality rate ratio 0.44 (95% CI 0.22-0.91), p = 0.027. The incidence rate of all causes hospital admissions was 19.60 per 1000 person-years in the intervention group compared to 33.45 per 1000 person-years in the control group, giving an incidence rate ratio (IRR) adjusted for age and gender of 0.61 (95% CI 0.44-0.84), p = 0.003. CONCLUSION:The implementation of SMC was associated with a substantial reduction in hospital admissions and all-cause mortality. Trial registration ClinicalTrials.gov NCT02646410.
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Project description:The onchocerciasis transmission models EPIONCHO and ONCHOSIM have been independently developed and used to explore the feasibility of eliminating onchocerciasis from Africa with mass (annual or biannual) distribution of ivermectin within the timeframes proposed by the World Health Organization (WHO) and endorsed by the 2012 London Declaration on Neglected Tropical Diseases (i.e. by 2020/2025). Based on the findings of our previous model comparison, we implemented technical refinements and tested the projections of EPIONCHO and ONCHOSIM against long-term epidemiological data from two West African transmission foci in Mali and Senegal where the observed prevalence of infection was brought to zero circa 2007-2009 after 15-17 years of mass ivermectin treatment. We simulated these interventions using programmatic information on the frequency and coverage of mass treatments and trained the model projections using longitudinal parasitological data from 27 communities, evaluating the projected outcome of elimination (local parasite extinction) or resurgence. We found that EPIONCHO and ONCHOSIM captured adequately the epidemiological trends during mass treatment but that resurgence, while never predicted by ONCHOSIM, was predicted by EPIONCHO in some communities with the highest (inferred) vector biting rates and associated pre-intervention endemicities. Resurgence can be extremely protracted such that low (microfilarial) prevalence between 1% and 5% can be maintained for 3-5 years before manifesting more prominently. We highlight that post-treatment and post-elimination surveillance protocols must be implemented for long enough and with high enough sensitivity to detect possible residual latent infections potentially indicative of resurgence. We also discuss uncertainty and differences between EPIONCHO and ONCHOSIM projections, the potential importance of vector control in high-transmission settings as a complementary intervention strategy, and the short remaining timeline for African countries to be ready to stop treatment safely and begin surveillance in order to meet the impending 2020/2025 elimination targets.
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Project description:Critically-ill patients with end-stage liver disease (ESLD) are at high risk for death during intensive care unit hospitalization, and currently available prognostic models have limited accuracy in this population. We aimed to identify variables associated with in-hospital mortality among critically ill ESLD patients and to develop and validate a simple, parsimonious model for bedside use.We performed a retrospective chart review of 653 intensive care unit admissions for ESLD patients; modeled in-hospital mortality using multivariable logistic regression; and compared the predictive ability of several different models using the area under receiver operating characteristic (AU-ROC) curves.Multivariable predictors of in-hospital mortality included Model for End-stage Liver Disease (MELD) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, mechanical ventilation, and gender; there was also an interaction between MELD score and gender (P < .02). MELD alone had better discrimination (AU-ROC 0.83) than APACHE II alone (AU-ROC 0.76), and adding mechanical ventilation to MELD achieved the single largest increase in model discrimination (AU-ROC 0.85; P < .01). In a parsimonious, 2-predictor model, higher MELD scores (OR 1.14 per 1-point increase; 95% CI 1.11-1.16), and mechanical ventilation (OR 6.20; 95% CI 3.05-12.58) were associated with increased odds of death. Model discrimination was also excellent in the validation cohort (AU-ROC 0.90).In critically ill ESLD patients, a parsimonious model including only MELD and mechanical ventilation is more accurate than APACHE II alone for predicting in-hospital mortality. This simple bedside model can provide clinicians and patients with valuable prognostic information for medical decision-making.
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Project description:BackgroundFebrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation.ObjectiveIn this study, we leveraged machine learning techniques to build a FN mortality risk evaluation tool focused on FN admissions without physicians' subjective evaluation.MethodsWe used the National Inpatient Sample and Nationwide Inpatient Sample (NIS) that included mortality data among adult inpatients who were diagnosed with FN during a hospital admission. Machine learning techniques that we compared included linear models (ridge logistic regression and linear support vector machine) and non-linear models (gradient boosting tree and neural network). The primary outcome for this study was death among individuals with a recorded FN admission. Model comparison was evaluated based on areas under the receiver operating characteristic curve (AUROC) and model performance was estimated using 30 % test set created via stratified split.ResultsOur analysis detected 126,013 adult admissions within the NIS data that were diagnosed with FN, among which 5,856 were declared as deceased (4.6 %). Our machine learning results demonstrate linear models and non-linear models achieved areas under the receiver operating characteristic (AUROC) around 92 % in survival prediction.ConclusionsWe developed machine learning models that do not require physicians' subjective evaluation for FN mortality risk prediction.
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Project description:Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.
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