Mortality After Peritonitis in Sub-Saharan Africa

Gallaher, Jared; Cairns, Bruce A.; Varela, Carlos; Charles, Anthony

doi:10.1001/jamasurg.2016.4638

Cited by 8 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IHDs have been associated with worse outcomes in both HICs and LMICs 7,10 . The overall mortality rate in the present study of 11⋅8 per cent was similar to rates of 10-19 per cent seen in other series 10,15,24,25 of abdominal operations in the region. In HICs, delays are often considered to have shorter intervals of less than 6 or 12 h 7,8,22 .…”

Section: Resultssupporting

confidence: 89%

“…Malawi is a landlocked country in south‐east Africa that can serve as a proxy for countries in the region. Access to surgical care is limited. The primary objective of the present study was to determine the effect of IHDs on in‐hospital mortality related to surgical interventions for emergency or urgent surgical conditions at a tertiary care centre.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of in‐hospital delays on surgical mortality for emergency general surgery conditions at a tertiary hospital in Malawi

et al. 2019

View full text Add to dashboard Cite

Background In sub‐Saharan Africa, surgical access is limited by an inadequate surgical workforce, lack of infrastructure and decreased care‐seeking by patients. Delays in treatment can result from delayed presentation (pre‐hospital), delays in transfer (intrafacility) or after arrival at the treating centre (in‐hospital delay; IHD). This study evaluated the effect of IHD on mortality among patients undergoing emergency general surgery and identified factors associated with IHD. Methods Utilizing Malawi's Kamuzu Central Hospital Emergency General Surgery database, data were collected prospectively from September 2013 to November 2017. Included patients had a diagnosis considered to warrant urgent or emergency intervention for surgery. Bivariable analysis and Poisson regression modelling was done to determine the effect of IHD (more than 24 h) on mortality, and identify factors associated with IHD. Results Of 764 included patients, 281 (36·8 per cent) had IHDs. After adjustment, IHD (relative risk (RR) 1·68, 95 per cent c.i. 1·01 to 2·78; P = 0·045), generalized peritonitis (RR 4·49, 1·69 to 11·95; P = 0·005) and gastrointestinal perforation (RR 3·73, 1·25 to 11·08; P = 0·018) were associated with a higher risk of mortality. Female sex (RR 1·33, 1·08 to 1·64; P = 0·007), obtaining any laboratory results (RR 1·58, 1·29 to 1·94; P < 0·001) and night‐time admission (RR 1·59, 1·32 to 1·90; P < 0·001) were associated with an increased risk of IHD after adjustment. Conclusion IHDs were associated with increased mortality. Increased staffing levels and operating room availability at tertiary hospitals, especially at night, are needed.

show abstract

Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Effect of in‐hospital delays on surgical mortality for emergency general surgery conditions at a tertiary hospital in Malawi

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Upon diagnosis with peritonitis, patients need immediate treatment (typically within 3 days), because it can progress rapidly and develop into life-threatening sepsis or septic shock [1]. Patients with peritonitis have higher mortality rates than those without peritonitis [17]. Several factors, such as age, sex, clinical conditions, and the living environment are associated with peritonitis-related mortality [17].…”

Section: Peritonitismentioning

confidence: 99%

“…Patients with peritonitis have higher mortality rates than those without peritonitis [17]. Several factors, such as age, sex, clinical conditions, and the living environment are associated with peritonitis-related mortality [17].…”

Section: Peritonitismentioning

confidence: 99%

Predictive Analytics for Caring and Managing Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complications Phenotypes (Preprint)

Sheng¹,

Hu²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

BACKGROUND Acute diseases have severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians’ care and management of acute disease patients by predicting crucial complication phenotypes for timely diagnoses and treatment. However, ensuring the clinical effectiveness and value for predicting complication phenotypes requires overcoming several challenges. First, patient data collected in early stages (e.g., risk factors) are less informative for predicting phenotypic outcomes. Second, patient data are temporally heterogeneous; e.g., patients receiving laboratory tests at different intervals and with varying frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for complication phenotype predictions. OBJECTIVE To predict severe complication phenotypes among patients suffering acute diseases, we propose a novel, deep learning–based method that employs recurrent neural network–based sequence embedding to represent patients’ disease progressions, with the consideration of temporal heterogeneities in patient data. The method incorporates a latent regulator to alleviate data insufficiency constraints, by accounting for the dynamics of underlying mechanisms that are not observed in patient data. A cost-sensitive analysis also addresses imbalanced outcome distributions in patient data for better predictions. METHODS From a major healthcare organization in Taiwan, we obtain a sample of 10,354 electronic health records that pertain to 6545 peritonitis patients. The proposed method projects these heterogeneous, temporal, clinical data into a substantially reduced feature space, then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Finally, our method applies a cost-sensitive analysis to improve predictive performance further. RESULTS To evaluate the proposed method, we examine its efficacy for predicting two hepatic complication phenotypes for peritonitis patients: acute hepatic encephalopathy (A-HE) and hepatorenal syndrome (HRS). We compare our method with three benchmark techniques. For A-HE predictions, our method attains an area under curve (AUC) of 0.74, which outperforms temporal case-based reasoning (T-MMCBR) by 48%, temporal short long-term memory (T-SLTM) network by 28%, and time fusion convolutional neural network (CNN) by 12%. For HRS predictions, it achieves an AUC of 0.65, which is 20%, 14%, and 18% better than that of T-MMCBR (0.54), T-LSTM (0.57), and time fusion CNN (0.55), respectively. Overall, the proposed method achieves higher recall values for predicting A-HE and HRS than the benchmark techniques. CONCLUSIONS The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes, and it offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios characterized by insufficient patient clinical data availability, temporal heterogeneities, or imbalanced distributions of important patient outcomes.

show abstract

“…In certain regions, the contrast in access to care between men and women is stark: Women cannot seek health care without permission from their husbands [3,5,6]. The consequences of disparities in access to surgical care are illustrated in patients with acute abdominal emergencies, where disparities leading to a delay in presentation for an Trista D. Reid trista_reid@med.unc.edu emergency can be life-threatening [7]. Highlighting this dilemma, a recent survey of Rwandan households discovered that approximately 30% of household deaths might have benefited from surgical treatment and prevented mortality [8].…”

Section: Introductionmentioning

confidence: 99%