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OBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study. SETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients' temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters. CONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.
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INTRODUCTION: Little is known regarding health care seeking behaviors of women in sub-Saharan Africa, specifically Cameroon, who experience violence. The proportion of women who experienced violence enrolled in the Cameroon Trauma Registry (CTR) is lower than expected. METHODS: We concatenated the databases from the October 2017-December 2020 CTR and 2018 Cameroon Demographic and Health Survey (DHS) into a singular database for cross-sectional study. Continuous and categorical variables were compared with Wilcoxon rank-sum and Fisher's exact test. Multivariable logistic regression examined associations between demographic factors and women belonging to the DHS or CTR cohort. We performed additional classification tree and random forest variable importance analyses. RESULTS: 276 women (13%) in the CTR and 197 (13.1%) of women in the DHS endorsed violence from any perpetrator. A larger percentage of women in the DHS reported violence from an intimate partner (71.6% vs. 42.7%, p<0.001). CTR women who experienced IPV demonstrated greater university-level education (13.6% vs. 5.0%, p<0.001) and use of liquid petroleum gas (LPG) cooking fuel (64.4% vs. 41.1%, p<0.001). DHS women who experienced IPV reported greater ownership of agricultural land (29.8% vs. 9.3%, p<0.001). On regression, women who experienced IPV using LPG cooking fuel (aOR 2.55, p = 0.002) had greater odds of belonging to the CTR cohort while women who owned agricultural land (aOR 0.34, p = 0.007) had lower odds of presenting to hospital care. Classification tree variable observation demonstrated that LPG cooking fuel predicted a CTR woman who experienced IPV while ownership of agricultural land predicted a DHS woman who experienced IPV. CONCLUSION: Women who experienced violence presenting for hospital care have characteristics associated with higher SES and are less likely to demonstrate factors associated with residence in a rural setting compared to the general population of women experiencing violence.
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INTRODUCTION: Risk factors for interpersonal violence-related injury (IPVRI) in low-income and middle-income countries (LMICs) remain poorly defined. We describe associations between IPVRI and select social determinants of health (SDH) in Cameroon. METHODS: We conducted a cross-sectional analysis of prospective trauma registry data collected from injured patients >15 years old between October 2017 and January 2020 at four Cameroonian hospitals. Our primary outcome was IPVRI, compared with unintentional injury. Explanatory SDH variables included education level, employment status, household socioeconomic status (SES) and alcohol use. The EconomicClusters model grouped patients into household SES clusters: rural, urban poor, urban middle-class (MC) homeowners, urban MC tenants and urban wealthy. Results were stratified by sex. Categorical variables were compared via Pearson's χ2 statistic. Associations with IPVRI were estimated using adjusted odds ratios (aOR) with 95% confidence intervals (95%CI). RESULTS: Among 7605 patients, 5488 (72.2%) were men. Unemployment was associated with increased odds of IPVRI for men (aOR 2.44 (95% CI 1.95 to 3.06), p<0.001) and women (aOR 2.53 (95% CI 1.35 to 4.72), p=0.004), as was alcohol use (men: aOR 2.33 (95% CI 1.91 to 2.83), p<0.001; women: aOR 3.71 (95% CI 2.41 to 5.72), p<0.001). Male patients from rural (aOR 1.45 (95% CI 1.04 to 2.03), p=0.028) or urban poor (aOR 2.08 (95% CI 1.27 to 3.41), p=0.004) compared with urban wealthy households had increased odds of IPVRI, as did female patients with primary-level/no formal (aOR 1.78 (95% CI 1.10 to 2.87), p=0.019) or secondary-level (aOR 1.54 (95% CI 1.03 to 2.32), p=0.037) compared with tertiary-level education. CONCLUSION: Lower educational attainment, unemployment, lower household SES and alcohol use are risk factors for IPVRI in Cameroon. Future research should explore LMIC-appropriate interventions to address SDH risk factors for IPVRI.